dataesr/scientific-paragraphs-categorization huggingface dataset:
Combined dataset In the combined dataset, the population estimate for CL (54. 3 l/h) was lower than that described for group 1 alone (84 l/h), and IIV on CL was higher (56 % in the combined, compared to 33 % in the group 1 dataset). Residual unexplained variability (RUV) was also higher for group 2 (53 %) than group 1 (13 %), and shrinkage was >45 % for all parameters apart from CL (27 %). Age was identified as the only significant contributor to IIV in CL (β_CL, age = -2. 9, p = 2. 3 e- 7 ). Parameter estimates for the best fit model are shown in Table  3 , and VPCs are shown separately for group 1 (Fig. 2b ) and group 2 (Fig. 2c ). Based on population PK parameters (in a person of standard 70 kg weight), the impact of age was such that CL in those aged 85 years (41. 8 l/h) was 54 % lower than those aged 65 years (91.
Discussion We have developed a population PK model for amisulpride in the elderly by combining data on healthy older people with a representative sample of AD patients, who were receiving low-dose amisulpride off label specifically to treat psychotic symptoms. The fact that PK data in group 1 was collected at multiple time points meant that it was possible to fully parameterise an oral two-compartment PK model and carry out covariate testing on all clearance and volume parameters. The final model explained 87 % of the variance in PK profile with excellent precision and identified gender as a significant contributor to the variability in central volume of distribution (V1). Gender differences in V1, which led to a higher Cmax in women and longer estimated t½ in men (15. 3 h compared to 13. 6 h in women), were not explained by differences in body mass, as weight was accounted for in the model through the use of allometric scaling, and height was compared against gender as part of covariate screening and model development.
Introducing allometric scaling into the covariate model  (Holford 1996; Mould et al. 2002 ) allowed CL to be estimated across the wide range of observed weights in the clinical dataset. Model simulations suggest that steady state blood concentrations at 50 mg amisulpride daily would increase from 30 to 85 ng/ml between the ages of 65 and 85 years in a 70-kg person, and from 40 to 120 ng/ml in a person of low (50 kg) body weight. These findings argue for the consideration of age-and weight-based dose adjustments in older people. Previously conducted clinical studies have shown an increase in CL and extended t½ in patients with renal impairment  (Rosenzweig et al. 2002). As a result, a 50 % reduction in daily dose is advised in moderate renal disease (GFR 30-60 ml/min) and a 66 % reduction in severe renal disease (GFR is 10-30 ml/min).
The PK model developed in this analysis included eight AD patients with moderate renal disease, a common finding in older patients, and one advantage of building the model from a representative clinical dataset. Our findings suggest that, for patients aged 65 years and above who do not have severe renal impairment, age and weight could be considered in place of renal status, to guide dose reductions. The published literature on amisulpride  (Mauri et al. 2014; Sparshatt et al. 2009 ) has consistently reported higher dose-corrected concentrations in women during TDM, and it has been assumed that gender differences in drug clearance contribute to this  (Coukell and Benfield 1996). However, data from the four large, naturalistic studies which have contributed most to our understanding of amisulpride PK are comprised predominantly of patients below 65 years and include minimal data on patients aged 80 years and above  (Bergemann et al.
As a result, compliance was carefully monitored (caregiver report and tablet counts) and facilitated by the clinical team. Despite the limitations of the AD dataset, it represents the first PK data on amisulpride within the context of therapeutic, steady state dosing in a clinical population who reached the extreme end of the age spectrum. The magnitude of the change indicates that therapeutic blood concentrations are achieved at lower doses and that age and body weight could be considered in place of renal status to guide dose adjustments. Obtaining additional steady state data on elderly people will help to further refine the model predictions regarding gender, concomitant medications and co-morbid health conditions, including pre-existing renal impairment. Previous studies which have used a population approach to explore PK profiles of psychotropic drugs in older AD patients have produced mixed findings.
For example, PK models developed using data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) trials for Alzheimer's disease (AD) and schizophrenia (SZ) have shown a significant effect of age on clearance of the active metabolite (9-OH risperidone) of risperidone  (Feng et al. 2008) , whereas interindividual variability in olanzapine clearance was accounted for by factors other than age (gender, smoking and African- are shown for the final model after analysis of group 1 data alone, overlaid to group 1 observed 5th, 50th and 95th percentiles (a), and after analysis of the combined dataset overlaid to group 1 observed 5th, 50th and 95th percentiles (b), and overlaid to group 2 observed 5th, 50th and 95th percentiles (c) American race)  (Bigos et al. 2008b ). These data, and a recent publication from the Citalopram in Alzheimer's disease (CitAD) study, which showed and clinically relevant effects of age and gender on metabolic clearance of Rbut not S-citalopram  (Akil et al.
Acknowledgments The project and salary of SR were funded by the  National Institute for Health Research (NIHR)  (PCDKBRA) during the course of the study. RH is also supported by the  NIHR UCLH BRC. We would like to acknowledge participants, carers and clinical teams in Mental Health for Older Adult services in the  South London and Maudsley NHS Foundation Trust  who made this possible, the research team, the  CRN  network who facilitated recruitment and the  London Pharmacometric Interest Group (University College London ) for their invaluable advice. Demographic and pharmacokinetic data obtained in 20 elderly healthy volunteers were provided by  SANOFI-AVENTIS R&D , from the report entitled "Amisulpride: study of pharmacokinetics and safety following a single oral administration (50 mg tablets) in the elderly" (SYNTHELABO report no.
Application We apply in this section the bargaining model to budget allocation decisions by French Water Agencies, as an interesting example of decentralized water management with negotiation between water user representatives in river basin committees. The ability to obtain closedform solutions from Proposition 1 derives from an reasonable assumption about the number of users as a special case, which will prove useful in our application. We first introduce the French water policy, and then discuss relevance of our bargaining model to this special case of environmental management. We then present the data used in the empirical application and introduce the special case of three user categories to derive an estimable system of structural equations. The structural estimation results are presented and compared with reduced-form estimation, including a non-nested test procedure.
This prior work suggests that more research is needed to develop methods for exploiting the value of social media data while overcoming their limitations. Here, we use micro-blogging data to extract networks of causal influence among different geographical sub-units before, during, and after collective social phenomena. In order to ground our work on empirical data, we analyze five datasets that track Twitter communications around five well-known social events: the release of a Hollywood blockbuster movie; two massive political protests; the discovery of the Higgs boson; and the acquisition of Motorola by Google. We selected these case studies because they represent different points in a theoretical continuum that separates two types of collective phenomena: those that can be represented as an endogenously-driven exchange of information; and those that respond more clearly to factors that are exogenous to the system.
I. RESULTS We consider the dataset concerning the time stamped and geolocalized time-series of tweets associated to the following events: the Spanish 15M social unrest in 2011; the Outono Brasileiro ("Brazilian Autumn") in 2013; the discovery of the Higgs boson in 2012; the release of an Hollywood blockbuster in 2012; the acquisition of Motorola by Google in 2011. All datasets cover a time-span preceding and following the event and details on data collection, including keyword selection and the geolocalization of messages, can be found in the Materials and Methods section and in the Supplementary Information (SI). FIG. 1 : Spatio-temporal activity as observed from the microblogging platform Twitter. Spain's 15M protest growth in time shows that the protest did not transcend the online sphere until May 15 th when the political movement emerged on the streets.
Broadcasting traditional media started reporting on it soon after; by that time, demonstrations had been held in the most important cities of the country. The spatio-temporal annotation of each tweet in the time series allows the construction of spatially localized activity maps that help identify, as time unfolds, the role that different geographical sub-units played in the global exchange of information. For each dataset the definition of the corresponding spatial unit is performed according to administrative and geographical boundaries as specified in the Material and Methods section. The time-stamped series of tweets originated from each spatial sub-unit X (supra-urban aggregates) defines the activity time series X t of the corresponding sub-unit in the social system. Timestamps are modified for each dataset to account for different time zones (see SI for details).
The only clear exception to this behavior is offered by the company acquisition dataset: the Google-Motorola announcement is a clear example of collective phenomena that is driven mostly by an exogenous factor, i. e. a media announcement. In this case, the dynamical time-scale is constant until the announcement is made public. In the SI we present the same analysis for the randomized signals, showing that time-scale variations are, as expected, washed out from the signal. The maximized information exchange can be analyzed at the level of geographical subunits by constructing the effective directed network  [29]  of information flow on a daily basis. This network is encoded in the matrix {T XY } that contains pairwise information about how each component in the system controls (or is controlled by) the others.
These units can mostly be identified with major cities; however the analysis also uncovers hidden drivers, such as Orotava, a less known urban area. Only after the first demonstration day on May 15 th the driving role becomes much more homogeneously distributed. In the Brazilian case, a set of clear drivers is present only during the onset phase preceding a demonstration on June 6 th , becoming fuzzier up to the major demonstration (June 17 th ) and totally blurred afterwards. We find a similar behavior in the Higgs boson cases (with rumors around the discovery on July 2 nd and final announcement on July 4 th )  [11]. The blockbuster case is driven by a steady excitement of the public before the movie release. Again, as expected, we observe completely different patterns in the case of the Google dataset.
The transition between such hierarchical or centralized driving to a symmetric scenario can be clearly identified monitoring the ratio θ = T † l /T † u between the sum all elements of T † in the lower triangle and the same quantity evaluated in the upper triangle. As schematically illustrated in Figure  4 , in a regime of perfect directed driving all the elements below the diagonal are zeros, i. e. , θ ≈ 0. In the opposite situation (i. e. the perfectly symmetric regime) the values below and above the diagonal are comparable, i. e. θ ≈ 1. The quantity θ can thus be considered as a suitable order parameter to characterize this order-disorder transition, thus helping to identify and differentiate communication patterns across the subunits of a system. Figure  5  shows the behavior of the parameter θ as a function of time in our five datasets.
In all the cases we initially observe a highly asymmetric effective network, where a few subunits have a dominant directional coupling to the rest of the system and θ 1. As the systems approach the onset date of the collective event, the quantity T † l /T † u undergoes a quick transition to θ ≈ 1 identifying a regime in which the couplings indicate the existence of collective phenomena where all subunits are mutually affecting each other. We see that in four out of the five datasets the system has a clear order-disorder transition occurring in the proximity of the collective event. Interestingly, in the case of the Brazilian protests the measure significantly increases before the main event (June 17 th ). Such behavior probably results from the effects of small precursor protests taking place from June 6 th onwards.
Additionally, the methodology presented here opens interesting paths to advance in the analysis of social phenomena and the identification of generative mechanisms; however, this advance should not be conflated with the possibility of forecasting the emergence of social events. The evidence we discuss is agnostic with regard to the predictive potential of online networks and micro-blogging platforms. A real predictive approach cannot be disentangled from an automatic selection of the relevant discussion topics. Our analyses use datasets that were already zooming into the right conversation domain and monitoring specific keywords/hashtags in the Twitter stream. We believe, however, that the general methodological framework we put forward is a first step towards a better understanding of the temporal and spatial signatures of large-scale social events.
III. MATERIALS Data. The first dataset focuses on the Spanish 15M movement, which emerged in 2011  [31, 32]. The data cover a dormant period of low micro-blogging activity that is followed by an explosive phase in which the movement gained the attention of the general public and was widely covered by traditional media sources (see Figure  1 ). The second dataset contains over 2. 5 million geolocalized tweets associated to the Outono Brasileiro ("Brazilian Autumn"), a set of political protests that emerged in Brazil in June 2013. Similarly to the Spanish case, the Brazilian data include an initial phase of low activity followed by a gradual escalation towards the high volumes of general attention that accompanied the street protests. The third dataset tracks communication on the discovery of the Higgs boson before and after it was officially announced to the press in July of 2013; this dataset has been used before to assess how rumors spread through online social networks  [11].
The fourth dataset contains messages related to the release of a Hollywood blockbuster, announced months prior to its premiere to stir momentum amongst the fan base. Finally, we also consider a dataset tracking communication on the acquisition of Motorola by Google, which came as sudden and unexpected news and immediately triggered a high volume of public attention. Spanish Twitter activity is spatially coarse-grained according to the list of metropolitan areas defined by the European Spatial Planning Observation Network  [35]. This process yields 56 aggregated time series: each of them corresponds to a different geographical area. In addition, there is an extra signal that accounts for any activity not included in those areas, i. e. the system is made up of N = 57 components. The data from Brazil are aggregated in 97 basins, which correspond roughly to metropolitan areas  [33, 34].
The data tracking rumors about the Higgs boson are aggregated at the country level, including only the N = 61 most active around this topic. Finally, the Motorola-Google and the blockbuster data are classified in 52 U. S. areas: 50 states, plus Washington D. C and Puerto Rico. Order-Disorder Transition. In real datasets the transition between the different scenarios can be visually inspected with a convenient sorting of the rows and columns of the T x,y matrix. We do so in Figure  5  of the main text, ranking each subunit of the system. The rank for a subunit x is assigned according to the number of times x it is dominant over the rest of the subunits. Once the ranking is settled, any T x,y < 1 2 T max x,y is set to 0 to improve the visual understanding of the figure. We then obtain a transformed matrix, i.
This prime-time televised address, however, did not placate citizens dissatisfaction, who continued staging protests in subsequent days. The dataset regarding such event has been obtained using the PowerTrack tool that provides 100% coverage for a set of specified keywords (see table  I ). For our analysis we considered just the tweets sent in the month of June 2013 (2, 670, 933 tweets). Indeed, the first large scale protest, often associated with the escalation of the protests, took place on June 17th, with remarkable (though smaller) precursors on the 6th and 13th. As in the case of the Spanish movement we considered a brewing period with low activity rates plus the "explosive" phase beyond the date of the first massive street protest.
C. The Higgs Boson Discovery Announcement (July 2012) In July 4 2012, a team of scientists based at CERN presented results that indicated the existence of a new particle, compatible with the Higgs boson (the existence of which had first been hypothesized in 1964). Mainstream news media covered the discovery after the announcement, but during the days preceding it there were already rumors of its discovery circulating through social media  [11]. The messages we analyze were collected using Twitter's publicly available API between July 1 and July 7 using a list of relevant keywords (i. e. lhc, cern, boson, higgs). In total, the data set contains 985,590 tweets.
D. The Hollywood Movie Release (July 2012) The Dark Knight Rises is the third installment of the Batman trilogy (started in 2005 with the release of Batman Begins and followed up in 2008 with The Dark Knight). It was premiered in New York on July 16 2012, and released in several English-speaking countries a few days later. The promotional campaign included so-called viral marketing through social media. The film was nominated to several prestigious awards, and grossed over a billion dollars in the box office. The dataset includes 130, 529 tweets between July 6th and July 21st that include the words "batman", "darkknight" or "darkknightrises". The tweets are obtained from the Twitter Gardenhose (a 10% random sample of the entire Twitter traffic).
E. The Google-Motorola Acquisition (August 2011) On August 15, Google announced a relatively unexpected agreement to acquire the mobile company Motorola. The move was a strategic attempt to strengthen Googles patent portfolio in a context where legal battles over patents is increasingly shaping the mobile industry and the telecommunications environment. The dataset contains 10, 890 tweets between August 5th and August 20th, 2011. In order to minimize the noise, we considered just tweets containing both "google" and "motorola". Also in this case, the tweets are obtained from the Twitter Gardenhose.
Tweets around the Higgs boson discovery were aggregated at the country level. The original dataset contained tweets from over 200 countries, but these have been thresholded to retain only those countries with more than 500 tweets over the topic of interest, for the whole week. This entails that only 61 countries are present in the analysis in the main text. The details regarding the location technique can be found in  [11]. The remanning two datasets have been aggregated in 52 areas -50 U. S. states, plus Washington D. C and Puerto Rico. In particular, the geographical information of tweets in this case has been gathered either from live GPS locations, or mining the so called "self-reported location". In general this field is filled freely by the users that can report their location at different levels.
B. Data temporal aggregation The definition of the temporal aggregation of Twitter data is particular important in our approach. Indeed, we want to determine the characteristic time scale at which the driving between series is most evident. The data comes with temporal resolution down to a second. However, such level of resolution is excessive to detect dynamical trends among series. We considered different sampling rates ∆τ spanning from 1 to 120 minutes. Although arbitrary, these range of temporal aggregations account for the fluidity of Twitter's discussion as well as the limited attention time span of users. In Section II D we discuss the ideas that allow us to determine, among the temporal aggregation schemes, the optimal one. and so on. Window width ω, admittedly, is the first parameter that will affect the measurements output, and we will discuss its effects later.
D. Defining the characteristic time scale of the events Given a certain dataset, we do not have any prior knowledge to define the correct timescale ∆t at which time series should be aggregated. We do know, however, that activity around civil protests (and in general around any event that involves collective action) are typically far from being stationary or periodic which intuitively points at the fact that there might not exist a single time scale for the whole dataset. Let's consider for now a fixed temporal resolution ∆t and bin the Twitter activity such that the first point in the time series will contain any activity that happened between [0, ∆t); the second point will contain data from [∆t, 2∆t), and so on. For example, a 30 day dataset, sampled at ∆t = 60 minutes will render a set of time series of length L = 30 × 24 = 720 points.
C. Sensibility analysis of the partition As mentioned above, the Twitter signal could be represented in many ways -and we have chosen a geographical approach. In fact, an enormous range of settings are possible: from a simple bipartition of the activity stream to a complete breakdown where a single user is matched to a time series. It is fair then to state up-front that our decision is an arbitrary one, driven by the obvious fact that geography matters in the offline and online worlds. Even within the geographical scheme, many options are available: spatial aggregation could be done at the neighborhood, city or county levels (for a finer resolution), or considering a coarser partition. For each dataset in the main text we have repeated the analysis for coarser geographical divisions. In the case of Spain's 15M movement, we have moved from the metropolitan areas to the autonomous community level (the Spanish 17 autonomous communities can be regarded as states, i.
To start with, the Brazilian dataset and the Batman event deliver dense T xy matrices, i. e. its sorted counterpart T has many below-diagonal elements even at early times, indicating that no (or little) transition takes place: the system is decentralized from the very beginning. These differences demand some explanation. First, it must be highlighted that our tip-over rationale (see subsection ??) is valid regardless the apparent contradiction: our claims are concerned with how the values in the T xy matrix are distributed, and as such it is an abstraction of what such matrix represents (be it cities, states or individuals, for that matter). Then, the apparent contradiction simply points at the fact that the lens through which we analyse the events does matter. Taking it to the extreme, a bipartition of the data, with two time series accounting for half of the activity each, would easily yield a fully symmetrical T xy matrix; in the opposite situation, a system comprising each user individually would render an (almost) empty matrix, given the fact that most people is not showing activity most of the time.