dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Longitudinal AE data, reported from a randomized, placebo-controlled clinical trial from a discontinued antipsychotic medication development program, were used in index development. The 72-day trial evaluated two doses of the discontinued medication bifeprunox (30 mg, n = 148; 40 mg, n = 148), an active reference medication (risperidone 6 mg, n = 154), and placebo (n = 149). Placebo was used as reference. By the nature of placebo, it was expected to have a lower burden score compared with active treatments. See Supplemental File 1 for details of calculations. An expert group, composed of three psychiatrists, a general practitioner, and two experts in data analytics, was assembled to review the study methodology and provide insights on the results. A total of three face-toface meetings were organized.
The psychiatrist's ratings were performed post hoc using AE data from a randomized, placebo-controlled clinical trial  [7]. For AEs not scored by the psychiatrist, importance factors were imputed by calculating the average factor for other AEs in the same system organ class. A total of 604 different AEs rated by the psychiatrist were reported in the study. To estimate their weight, 462 were estimated by system organ class average and 142 were estimated by overall importance factor average because their system organ class did not correspond to any of the system organ class AEs scored by the expert. Higher importance factors indicate a greater perceived AE impact. AEs leading to discontinuation were assigned an importance factor of 1. 0 for that patient. See Supplementary Table  1  for assigned importance factors for AEs.
Discriminatory capability of burden scores The discriminatory capability of adjusted versus unadjusted burden scores was evaluated using AE-related discontinuation data from the selected clinical trial. Mean burden scores (unadjusted, adjusted by experienced impact, adjusted by severity, adjusted by severity-adjusted duration, and adjusted by both experienced impact and severity-adjusted duration) were calculated for patients who discontinued because of an AE (n = 73; 14%) or who did not discontinue because of an AE (n = 526; 86%) to validate the analysis of AE burden and the determination of burden scores. The discriminatory capability of unadjusted and adjusted burden scores was analyzed using odds ratios calculated by logistic regression for patients who did or did not discontinue treatment.
Discriminatory capability of burden scores The discriminatory capability of unadjusted and adjusted burden scores was evaluated using data for subgroups of patients who discontinued or did not discontinue treatment because of AEs. Mean burden scores were higher for patients who discontinued than for those who did not, regardless of burden score adjustment (Table  3 ). The ratio of scores for patients who did versus did not discontinue because of AEs was greater for adjusted burden scores than for unadjusted burden scores. Percentages of patients who discontinued because of AEs were similar across treatment arms; however, burden scores were significantly higher in the discontinuation subgroup and could be used to discriminate between patients who discontinued because of AEs compared with those who did not (Table  4 ).
Proof-of-concept testing was performed using AEs reported from a randomized, placebo-and active-controlled clinical trial of an antipsychotic medication that was in development for schizophrenia; for these initial development efforts, patient perspectives were estimated by an experienced physician. As this model is refined and tested using additional clinical study data, it will be important to incorporate information obtained directly from patients. Burden score adjustments were calculated and their discriminatory capability was evaluated by comparing subgroups of patients who discontinued versus did not discontinue because of AEs; next, tolerability index scores were calculated based on adjusted burden to normalize scores on a scale of 0 to 100. A major a Data are presented for 1-unit increments.
Together, these approaches will provide a comprehensive approach to assessing treatment tolerability that captures relevant AEs and patient-level perspectives. External validation of the tolerability index will be performed using a scale such as the UKU  [18]  or LUNSERS  [19]  self-assessment tool. A benefit of the tolerability index approach is that it can be adapted to other treatments or conditions such as major depressive disorder. AE weighting based on patient-experienced impact must be adjusted to account for the side-effect profiles and patientreported tolerability of the medications and disease states assessed; however, the index can be implemented retrospectively and without any additional data collection. Further, AE data collected during a clinical trial were used in development of the tolerability index algorithm; the index can also be used for datasets, including only treatment-related AEs (i.
Conclusions The proposed tolerability index incorporates a more patient-centric method of AE reporting that reflects the fact that most patients experience multiple different AEs. Although further algorithm validation and elicitation of patient-reported importance factors for the perceived impact of the AEs are ongoing, these initial proof-of-concept analyses suggest that standardized scoring of treatment burden and tolerability is both possible and clinically relevant. The tolerability index approach may enable quantitative comparison of tolerability profiles across multiple medications used in treating patients with schizophrenia. The approach described here has the potential to be used on any dataset without the need for additional data collection, and can be adapted for use in evaluating the tolerability of medications used to treat a variety of conditions.
Characteristics of the participants Eighty patients were recruited, of whom 7 had nonassessable data, 12 did not undergo the CSS interview and 1 was a "suspect" respondent. Hence, 60 patients (47 female = 78%) were fully assessed (Fig. S1 1  ). Excluded patients presented no differences from the recruits. After a median follow-up of 44 months following inclusion, none of the patients had developed a systemic lupus, including those with positive antinuclear antibodies. One hundred and sixty controls were recruited. The data of 40 controls, matched with those of excluded patients, were omitted. In addition, one control with non-assessable data and one with a "suspect" questionnaire were excluded. Hence, the patients were compared with 118 controls. The characteristics of the participants are described in Table  I.
Co-occurring axis I psychiatric disorders As personality disorders were frequently associated with Axis I psychiatric disorders, we investigated the proportion of patients who had this co-occurrence. Eleven patients (18%) had PDs alone and 12 (20%) had at least one co-occurring psychiatric disorder. We therefore decided to look for the most frequently associated psychiatric and personality disorders (Table  SI 1  ). Fifty-two percent of patients with at least one PD also had a psychiatric disorder as against 38% of patients without PDs. Almost all PD categories were associated with psychiatric disorders. Unfortunately, because of insufficient data it was not possible to assess significant differences.
The stimulus polarities were alternated across trials in order to minimize the contribution of stimulus artifact and cochlear microphonic  39, 41. One of the authors (BI) was in charge of data acquisition in both countries using the same portable EEG system. This prevents the possibility of having a bias due to different experimental setups, participant preparation, and instructions. Data analysis. All analyses were performed using custom MATLAB scripts (MATLAB R2013b, MathWorks Inc). First, electrophysiological recordings were bandpass filtered from 70 to 2000 Hz (12 dB/octave roll-off) using a Butterworth filter. Then, sweeps with activity exceeding ± 30 µV were rejected as artifacts and the responses were baseline-corrected to the pre-stimulus period (-30 to 0 ms). Neural responses were then averaged over a -30 to 230 ms window.
25]. Accordingly, scholarly assumptions about the timing of significant anthropogenic impacts on tropical forests generally point to the post-industrial era or, at the earliest, the colonial era of European 'discovery'  [26, 27]. Clearly, the accumulating database of archaeological and palaeoecological evidence for pre-industrial and pre-colonial tropical forest occupation and transformation has not been effectively communicated beyond a restricted set of sub-disciplines [though see  refs 28-31]. As a consequence, this evidence has only played a small role in discussions about the start date or characteristics of the Anthropocene [for example, ref. 32 , but see ref. 33]. Here, we review evidence that has accumulated, primarily in recent decades, for the long-term human transformation of tropical forest ecosystems.
That early foragers could have played a significant role in reshaping newly colonized landscapes is also supported by evidence that later foragers did. For example, the first colonists of the eastern Caribbean in the mid-Holocene brought their foraging, collecting and hunting lifestyles with them, and engaged in modification and management of tropical ecosystems that is reflected in significant shifts in pollen and phytolith datasets  [50 ]. Still debated, but potentially even more significant in terms of long-term impact, is human involvement in Late Pleistocene tropical forest megafaunal extinctions, which are argued to have had anthropogenic, climatic or multivariate causes, and to have resulted in major changes to ecosystem structure  [47]. While discussions of megafaunal extinctions in tropical forests have been relatively limited, these environments possessed diverse megafauna, some of which persists in parts of Africa and Asia  [51 ].
Implications for the twenty-first century Although tropical forests were once seen as pristine, they are increasingly becoming recognized as outcomes of long-standing human modification, management and transformation. New methods and emerging datasets are demonstrating unequivocally that their enduring transformation by past human populations has much greater antiquity than previously thought. Yet despite the contemporary threat to tropical forests, and the need for concerted cross-disciplinary efforts to address the challenges they face, growing archaeological datasets have to date played only a relatively minor role in shaping contemporary discussions, debate, and policy-making. This is in part a result of limited archaeological survey and exploration of tropical forests relative to other environments.
Acknowledgements We would like to thank the participants of the Pantropica 2016 workshop, funded and hosted by the  Department of Archaeology at the Max Planck Institute for the Science of Human History, Jena , for taking part in an international meeting devoted to the global archaeology of rainforest environments. We would also like to thank  N. Sanz , and the  UNESCO office in Mexico , for invitations to tropical forest conservation workshops in Xalapa (2015) and Mexico City (2017). The discussions that took place during these three workshops informed and shaped the early stages of this manuscript. We would also like to extend our thanks to  N. Hofer  for her help with Figs 1, 2 and 3. D. E. 's contribution, and  N. Hofer 's contribution to the illustrations, were funded by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No 639828) in partnership with the  APSARA National Authority  and the  Ministry of Culture and Fine Arts, Cambodia.
Methods This study was conducted at Paris 8 University in France during the 2016/2017 academic year, with students in English for Arts courses at A2 and B1 levels. These courses are designed based on the teacher's area of expertise and experience, sometimes in consultation with the Arts department. The levels are based on the Council of Europe's (2001) framework for language proficiency. To provide a basis for comparison, motivation data from students in GE courses were also included. In order to increase the validity of our results  (Dörnyei, 2011) , we used a mixed methods approach -via questionnaires, interviews, and observation schemesfor collecting data on student motivation and attitudes in their English classes.
Questionnaire The motivation questionnaire we used was adapted from previous L2MSS studies  (Brander, 2013; Tort Calvo, 2015; You & Dörnyei, 2016)  to create a tool for studying learner attitudes towards the learning environment. The final version of the questionnaire contained 27 items. As this data is part of a larger study, only answers from the six questions measuring the L2LE are reported here 3. A seventh item was eliminated from consideration due to an anomaly in the printed version. The questionnaire was given to students in French and was distributed to some via email and to others in paper format. In total, it was sent to 42 GE A2 students, 64 GE B1 students, 55 ESP A2 students, and 61 ESP B1 students. Their participation was optional. It was deemed necessary to provide a shorter questionnaire than was used in past studies, given a low response rate of about 50% from the pilot session.
Questionnaire data Table  2  shows the proportion of students' positive reactions to different aspects of the classroom environment. For example, the figure regarding teacher/student relationship comes from the number of respondents marking 4 or 5 on the Likert scale for that item. For True/False questions, the figure is based on the number of respondents answering True. These results tend to show that students in all groups have similar attitudes towards their courses, with most being positive. Still, a majority do not feel a sense of excitement when going to class, and large numbers admit to giving into distractions. Aside from these measures, it seems many students feel their class is useful, practical, and that the teacher creates a good classroom atmosphere. The questionnaire also contained one open-ended question related to the learning environment, asking students to comment on an activity they found particularly useful.
Observation data Table  3  below contains percentages showing how often each student exhibited each type of motivation over the course of 18 hours of observation, made up of six three-hour observation sessions. It also shows how often students changed between active and passive engagement and disengagement, and the average number of changes per hour. In ESP A2, Student 1's interview answers (reported in the previous section) reinforce her observed behaviours shown in Table  3. Six instances of disengagement were observed, of which four corresponded to moments in which students spent extended periods on one activity; in each of these cases, the activity was a specialised activity, relating to art. The other two instances occurred at the beginning of the class, as the teacher presented the day's lesson; these instances are likely explained by her comment about sometimes texting out of habit.
Discussion The present study seeks to shed light on motivating elements in specialised language courses to better understand their motivational value in relation to GE courses. A combination of questionnaires, interviews, and classroom observations helped to identify some elements of a course that students found motivating. Considered together, the above results support  Henry's (2015)  insistence on considering numerous factors when analysing student motivation and validates the use of CDST. Students reacted to the duration of the class, work with their peers, the timing of class activities, and the structure of different tasks. As mentioned previously, questionnaire data showed that students in all four groups had mostly positive attitudes towards their courses (considering them useful and practical), even though most admitted not being excited about going to class, as well as being easily distracted during a lesson.
Further research will focus on who actually uses the resources the most (A2, B1, B2, or C1 learners?), and when they use them (pre-task or post-task). Is it those who need them the most in order to succeed in their first year Business English courses (A2/B1 learners)? Or is it those who need them the least (B2/C1 learners)? Previous research has indicated that those who have higher levels are those who tend to use them the most as they are also those who tend to be the most autonomous in their learning  (McAllister, 2013; Prince, 2009). Further pedagogical development will focus on enriching the VRC so as to offer students an extensive database of contextualised activities on a wide variety of problematic structures, adapted to all levels of the CEFR. Beyond developing the resources, making the students more aware of their failings in performing the different tasks may help them understand the importance of having more autonomous attitudes toward language learning, and may encourage them to use the resources available to them.
Acknowledgements We would like to express our appreciation to the authors of the chapters in this book for their valuable contributions, to the members of our editorial board who generously gave their time and expertise to review them, and to  Professor Dan Douglas  for accepting our invitation to write a foreword to this collection. We are grateful to a number of institutions and organisations for their sponsorship of this project: the  Université Paris-Sorbonne , especially the  CeLiSo (Centre de Linguistique en Sorbonne ,  EA7332 ), the  Ecole Doctorale V -Concepts et Langages - and the  ESPE de l'Académie de Paris , as well as the  Université Côte d'Azur. We owe a special debt of gratitude to the  French association for ESP  in higher education, the  GERAS (Groupe d'Etude et de Recherche en Anglais de Spécialité ) for encouragement as well as financial support.
Acknowledgements The materials for this course were developed and taught by a team of teachers at the  Département de Langues et Cultures, Bordeaux University. We thank our colleagues,  Valérie Braud ,  Anne-Laure Damongeot , and  Thibault Marthouret  for their valuable input throughout the design of this course. Acknowledgements Many thanks to  Erin Cross ,  Jonathan Upjohn , and the two anonymous reviewers for their insightful comments. Acknowledgements We would like to thank the two editors of this volume,  Shona Whyte  and  Cédric Sarré , as well as the two anonymous reviewers for their insightful and constructive suggestions. We also thank  Tamar Kremer-Sadlik  for her helpful, invaluable advice and for our rich discussions throughout the Science and  Storytelling Project.
δ 18 O analysis The oxygen composition in enamel apatite carbonates (δ 18 O C ) from the first (M1) and the third (M3) molars has been analysed for the three individuals. The analyses were carried out by GNS Science (Wellington, New Zealand) and the results were reported on the V-SMOW scale. The δ 18 O C data was then converted to δ 18 O w (oxygen composition in water/precipitation) by using equation 6 from Daux et al. [17]  modified by Chenery et al. [18]. Application of this conversion leads to a model uncertainty of ±1. 0‰ (2 σ) according to Chenery et al. [18]. Breastfeeding affects the oxygen composition in the body  [19] [20] [21]  and, consequently, the oxygen composition in the enamel of M1, as the crown of this tooth forms during the period when children are normally breastfed. The timespan for breastfeeding would have been similar during the Middle Ages  [22].
A 7500 Real-Time PCR System and 7500 Software v2. 0. 6 were used in both quantification assays. Molecular genetic sexing was performed using redundant gonosomal information (Amelogenin, SRY-gene, X-STRs) according to the GenderPlex protocol detailed in Esteve Codina et al. [31]. For the data analysis GeneMarker HIDv1. 70 Software was used. Y-chromosomal DNA in the male sample was typed with the PowerPlex Y23 System and the Yfiler Plus PCR amplification kit. Raw data were analysed using GeneMapper ID-X Client Software. The consensus Y-STR haplotype was assigned to the respective Y-chromosomal haplogroups (hg)  [32, 33]. The mtDNA control region (CR) was sequenced for all samples using the mini-multiplex assay designed by Berger and Parson  [34]. All sequences were run on a 3500xL Genetic Analyzer.
There are a number of anatomical and anthropometrical standards that are employed during the facial depiction process, and these rely on the interpretation or measurement of skeletal structure to estimate soft tissue features. In this way the orbits are interpreted to predict the eyes, the nasal aperture is measured to predict the nose, the teeth are analysed to predict the mouth and the mastoid processes are assessed to predict the neck and ears. These standards  [37] [38] [39]  have been established from studies relating to human dissection  [40] [41] [42] [43] [44] [45] [46] , clinical imaging (CT, MRI and X-rays) of living subjects  [47] [48] [49] [50] , palpation studies  [51, 52]  and anthropometry  [37, 53]. In addition, tissue depth data at anatomical points across the skull are utilised to act as guidelines for the amount of soft tissue over the skull as related to sex, age and ethnic group.
Throughout the 20 th and 21 st centuries clinical imaging methods have been utilised to measure living subjects, starting with planar x-rays and eventually advanced 3D imaging, such as computed tomography, Magnetic Resonance Imaging, ultrasound, low dose cone-beam computed tomography and holographic topometry. There are many sets of data available from different in vivo ethnic groups across the world for use in craniofacial analysis. There are data for adults from a variety of ethnic groups in Germany, Belgium, the former Soviet Union, Slovakia, Japan, South Korea, The Netherlands, USA, Egypt, Chile, Turkey, India and South Africa. There are datasets for children from the USA, the UK, Japan and the Philippines. Collations of these datasets can be found in some publications  [38, 54]  and at some websites (www.
craniofacialidentification. com; www. craniofacial-id. com). The skulls were scanned using Artec Space Spider, which is a high-resolution 3D scanner based on blue light technology. The scanner's ability to render complex geometry, sharp edges and thin ribs means it is an ideal industrial 3D scanner for high resolution capturing of objects followed by the export of the final 3D model as an obj. file. The scan data was translated into 3D digital models of the skulls in order to produce facial depictions in our 3D computer system. The system employs Geomagic Touch X hardware and Freeform Modelling Plus software. This system has haptic feedback and allows the practitioner to feel the surface of the model that can be seen on the monitor. A database was accessed for the depiction process containing pre-modelled muscles and anatomical structures created for use in forensic reconstruction.
33‰ (±1‰), which is compatible with Trondheim. Taking into account the possible effect of breast-feeding effect on the M1 tooth, they have calculated the isotopic gradient the individuals crossed between early childhood and teenage. They have shown that this woman was neither born in, nor near, Trondheim where she ended her life, but in an area where the environmental water was 6‰ to 7. 3‰ lighter than in Trondheim, that is with δ 18 O w of about -15 to -16. 5‰. The δ 18 O w values of mean precipitation (GNIP database) and surficial waters  [61]  range between about -11‰ and -14‰ in Scandinavia, except in Naimakka in the county of Norrbotten, northern Sweden (-16. 6‰), which would have similar environmental and climatic conditions to the inner regions of the county of Finnmark in Norway and the northwesternmost parts of Finland.