dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion We propose a method for building accurate association rules from an EHR by linking different pathological conditions and medications. After data preparation, association rules were built and then post-processed to retrieve the relevant and operational rules. We validated our method on a subset of association rules (one medication implies one disease code). Our primary goal is to create a complete set of rules, containing multi-elements rules (including several items in the antecedent or in the consequent). The final goal is to provide a tool to improve drug-disease knowledge databases. The results we present here tested the application of this method on 1,440 patients. Indeed, we developed a tool based on quality measures to rank the rules. However, these ranked rules must be reviewed by a human (physician) for definite validation, in the absence of any existing reference for multi-elements rules.
When there is a strong comorbidity between two conditions, for example hypertension and diabetes, we can have a consequent part of the patients presenting the two conditions and the two treatments. This can lead to transitive association. Irrelevant rules resulting from the noise produced by the extraction of pathological conditions. Compared with similar published methods, this method is applied to a very different type of dataset. For example, Wright and al. [17]  worked on a database consisting of 100,000 patients with 272,749 health conditions and 442,658 medications. In comparison, our complete database was approximately 100 times smaller (1,440 patients). However, our database was only 10 times smaller for conditions (25,180 ICD codes) and 30 times smaller for medications. When considering only the different codes, the 1,756 conditions for Wright and al.
becomes 1,170, and 2,128 medications becomes 396. Because we worked in a specific hospital ward (which was specialised), we retrieved proportionally less different medications than in a whole hospital and also retrieved fewer diagnoses. The fact that there were the same number of codes between a hospital data set (100,000 patients) and a cardiologic database (1,400 patients) indicates that our data may be noisier. Indeed, we worked with text mined diagnoses from discharge letters and not with structured information gathered in EHR. These considerations could explain why the quality measures were not as good as expected. Our future goal is to reduce the noise generated by this method to improve the classification precision. It will be interesting to compare this method with the association rules built with diagnoses extracted from structured elements of the EHR.
2. For medications, the ATC classification is a hierarchy of five levels. Currently, generalised association rules require further research to improve our method. To validate our method, we used only disease codes and medications from one specialised hospital ward to have a more homogeneous medical practice. Using this validated method, the next step is to build rules based on the whole hospital. Moreover, all the structured items in the EHR, such as laboratory results or data imaging are candidates for association rules mining.
Study Participants and Clinical Assessment Participants aged 65 or older were recruited within the Dem@care protocol at the Nice Memory Research Center located at the Geriatric department of the University Hospital. The study was approved by the local Nice ethics committee and only participants with the capacity to consent to the study were included. Each participant gave informed consent before the first assessment. It was a non-randomized study involving three diagnosis groups of participants. The video data of 49 participants were exploitable from which 12 patients were diagnosed with AD, 23 patients diagnosed with MCI, and 14 healthy controls (HC). All diagnoses were made by a medical doctor from the Geriatric University Hospital. For the AD group, the diagnosis was determined using the proposed diagnostic criteria from  Dubois et al.
Data Collection and Processing Participants had their activity recorded using a color-depth sensor placed close to the ceiling of the ecological room to maximize its coverage of the room. Recorded data were posteriorly analyzed by an event monitoring system (EMS, see Figure  1 ) to automatically extract fine-to coarse-grained information about patient's performance (e. g. , feet position, number of steps, the IADL carried out). Using the automatically extracted information, we estimate gait-and IADL-related parameters to describe the participant performance in the clinical protocol. The estimated parameters were then used as input features for Naïve Bayes model to classify the participant's performance into the autonomy and dementia classes investigated in this work. Targeted autonomy classes were good, intermediate, and poor; and targeted cognitive status classes were Alzheimer's, MCI, and healthy.
For more details on IADL modeling, please refer to the work of Crispim-Junior et al. (2013). Figure  3  presents the monitored scene annotated with the semantic information used for event modeling and recognition. Left image displays the recognition of watering plant event. The output of the EMS is the basis for the computation of the performance of the patients in the clinical protocol. From its output data (event report), we extract descriptors with different levels of granularity to appropriately describe the patient performance according to the complexity of the monitored activity. For gait analysis, we estimate fine-grained features like stride length, distance traveled, average speed, and cadence for the period of time of the physical task events (e. g. , mono and dual task events). For the IADL, we compute their frequency and duration, and the number of times the patient missed or repeated them.
Statistical Analyses In a separate step, next to the video data extraction analyses, the characteristics of all participants as well as the annotated performance results of the ecological assessment were analyzed in order to determine the different autonomy levels. Comparisons between the groups (e. g. , HC subjects, MCI patients, and AD group and good performer, intermediate, and poor performer) were performed with Mann-Whitney tests for each outcome variable of the automatic video analyses. Differences were reported as significant if p < 0. 05. Spearman's correlations were further performed to determine the association between the extracted video parameters and established assessment tools, in particular, for executive functioning, e. g. , the FAB.
Since then, it was revisited by several authors who suggested different ways of interpreting and using it  [46, 47]. These authors understood that there must be several feedback loops in the data-information-knowledge pyramid. For example, the ability to select relevant information from an excessive quantity of available information demands orientation knowledge (the aims and purposes must be known). Furthermore, Jennex  [47]  recognises that while data and information are clearly the bricks of knowledge, productive knowledge is needed to produce data and information in the first place. In this way, knowledge production becomes more cyclical than linear. Concerning the DIKW framework, a few issues require further discussion as the geometric form of a pyramid (linear rather than cyclical) may be misleading.
For this reason, Fig. 2  illustrates the framework as a web with interweaving bi-directional threads so as to show that data, information and knowledge are intertwined more closely than a pyramid can illustrate. In accordance with perception and context, data is collected to improve understanding of a certain reality. Knowing the context in which data is collected and transformed into information (contextualised, categorised, calculated, connected and/or condensed) is highly significant  [46] : why is this data collected? Is sufficient understanding and knowledge of the methodology for data acquisition available (feedback loops between knowledge and data)? For what purpose is the data being prepared as information (understanding the use of the data and information)? This requires the data analyst to not only have a profound understanding of the data itself (including the limitations), but to be aware of the wider systemi.
A further common attribute and essential criterion linking each instance of the DIKW web, which is required to ensure the transfer from one state to the next, is the use of intellect. Although this may appear to be a vague statement, intelligence is necessary in order to acquire data in a correct and accurate manner using state-of-the-art means, as well as producing information in consideration of the context. Again, the intended meaning of the information may only be received using intelligence. As an example, information provided to reduce the level of future flood losses needs to be understood with reference to this intention, rather than the intention to reduce potential damage caused by landslides. The third element depicted in Fig. 2  is learning, which extends all the way through the spider web.
To do so, information related to disaster management in a given case study must be generated by interviewing relevant experts and stakeholders as well as conducting a comprehensive literature review, which includes newspaper articles and online news outlets. The results are classified according to who (stakeholders) does/did what before, during, and after a specific disaster event. In a second step, these experience reports are analysed using the Disaster-Knowledge Matrix (DKM) to identify barriers and bridges in knowledge production and use with regard to the four stakeholder groups. Information pertaining to the respective management stages of the DMC is produced using different data and information as input, and various methodologies. The resulting information and knowledge gained during a certain phase provides a basis for decisions to be taken by the respective decision-makers and stakeholders.
Acknowledgements We would especially like to thank the local interview partners for their input. The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2013-2015 ) under Grant Agreement no. 603807  (KNOW-4-DRR, http://www. know4drr. polimi. it/). We are grateful to  Dominic Spiekermann  for valuable discussions on the philosophical aspects of knowledge presented in Section 2. We also thank  Hans Wiesenegger  and  Bernhard Niedermoser  for the details they provided on the Salzburg Flood 2013.
Acknowledgements This work has been supported by the  Helmholtz International Center for FAIR  within the framework of the  LOEWE program. We also acknowledge support from Grants No. FPA2010-16963  ( Ministerio de Ciencia e Innovación) , No. FP7-PEOPLE-2011-CIG  under Contract No. PCIG09-GA-2011-291679 , No. FPA2013-43425-P , and the  European Community - Research Infrastructure Integrating Activity Study of Strongly Interacting Matter (acronym HadronPhysics3 , Grant Agreement n. 283286) under the  Seventh Framework Programme  of  EU. D. C. acknowledges support from the  BMBF (Germany)  under project No. 05P12RFFCQ. L. T. acknowledges support from the  Ramón y Cajal Research Programme (Ministerio de Ciencia e Innovación).
As a whole, therefore, it remains the case that the larger number of cell divisions required to build a large body creates cancer risks that larger-bodied organisms need to cope with. Peto's paradox suggests that solutions have been found  [11] , and the same message arises in a recent study among tissues in humans: Noble et al. 's reanalysis  [15]  of the Tomasetti & Vogelstein dataset  [16]  reveals that cancer incidence saturates with the total number of stem cell divisions. This is suggestive of a role of natural selection either in limiting the size or stem cell replications in the most cancer-prone tissues and/or in lowering the probability of obtaining cancer through specific mechanisms in these same tissues. Given the diversity and complex interlocking of different life-history traits, and their ecological and evolutionary interactions with carcinogenesis, deciphering simple causal pathways is likely to be challenging.
Towards 'cancer-aware' life-history modelling Absolute cancer risk (hereafter 'cancer risk') is the probability that a person contracts the disease over a fixed period of time, usually for a range of specific ages or over a lifetime. Cancer risk is generally calculated from epidemiological data, but how could it be mechanistically modelled? There are various suggestions in the literature  [27]  as well as recent empirical evidence  [16]  that, in humans, cancer risk scales straightforwardly with the total number of cell divisions. A linear fit to the full dataset in Tomasetti & Vogelstein  [16]  gives: 10 11 divisions ! approximately 10 -1 lifetime risk; 10 7 divisions ! 10 -4 lifetime risk. On a log-log scale one could derive a simple prediction that if increasing body size by a factor of 10 requires 10 times as many cell divisions (an assumption that we shall discuss in §5), then each magnitude of body size increase should lead to 10 3/4 ¼ 5.
6-fold cancer risk. It also follows that body sizes beyond approximately 10 12 stem cells should not exist in nature, as cancer risk would then reach 100%. (A human being is estimated to contain 3. 72 Â 10 13 cells  [28] , but not all of these are obviously stem cells. ) However, incidences in Tomasetti and Vogelstein's dataset begin to saturate in tissues beyond approximately 10 9 total cell divisions, suggesting either that natural selection has resulted in these tissues being differentially protected from cancer, and/or constrained in size and/or other mortalities occur before cancer, meaning lower effective cancer incidence  [15]. Noble et al. [15]  additionally showed that when considering families of tissues, their slopes were approximately unity, further indicating that natural selection can explain the overall pattern.
2. 5, leading to 350 sequences of 16S (303 bp) and COI (654 bp) which were concatenated to perform analyses. Haplotypes were retrieved using DNASP v5 both for individual markers and concatenated data  [58]. Haplotypes for individual markers were deposited in GenBank (accession numbers: KM208862-KM208879). Seven microsatellite loci (msat) were used as nuclear co-dominant molecular markers: DikS, DikF  [52] , Dv11, Dv13, Dv17, Dv31, Dv33  [59]. A total of 876 specimens were genotyped, locus DikF amplified only for Danube and Western Route. PCR conditions were described by Wattier et al. [52]  and Rewicz et al. [59]. Microsatellite alleles were visualized in 6. 5% acrylamide 25 cm long gels on a LICOR 4200 L automated sequencer and scored by eye. Reference individuals were included for inter-gel calibration.
Both runs were examined using Tracer v 1. 6, all sampled parameters achieve sufficient sample sizes (ESS>200). Tree files were combined using Log-Combiner 1. 8. 1  [63] , with removal of the non-stationary 10% burn-in phase. The maximum clade credibility tree was generated using TreeAnnotator 1. 8. 1  [63]. To add additional support for the tree topology, the same dataset was analyzed with Maximum Likelihood (ML) method based on the General Time Reversible (GTR) model  [65]  with 10000 bootstrap replicates. Model of evolution was selected using JMODELTEST2  [66]. ML analyses were performed in the MEGA 6  [61].
Genetic diversity and F ST were assessed either pooling sampling sites, or not, according the hypothesis tested, e. g. between fronts in Poland. Population structure was also analyzed using individual-based Bayesian clustering method implemented in STRUCTURE 2. 3. 4  [74]. Simulations were performed on the full data set including 29 populations and 876 individuals. Runs for each possible value of K (1 to 8) were repeated 20 times. Each run used a burn-in of 500000 iterations, a run length of 750000 iterations. All simulations were performed using the admixture and correlated allele frequencies models with no prior information. Selection of most probable value of K relied on the ΔK method developed by Evanno et al. [75].
Diversity and differentiation along Western Route (WR) Based on 20 sites along the WR we tested if microsatellite differentiation increased positively with distance between sites (isolation-by-distance, hereafter IBD) but also if diversity (mean allelic richness) was associated with geographical distance from the source area (Danube delta). The distances were estimated using GOOGLE EARTH v. 7. 1. 2. IBD was tested using Mantel test between F ST / (1-F ST ) and geographic distance as recommended by Rousset  [76]  for testing IBD in one-dimensional linear systems, with 100000 permutations, using the GENEPOP on the Web 4. 2  [77]  and ISOLDE software.
Acknowledgments Authors thank  Alicja Konopacka ,  Nina Fielding ,  Paul Stebbing ,  Jamie Bojko ,  Krešimir Žganec ,  Tomasz Mamos  and  Radomir Jaskuła  for their help in collecting the material for this study. Special thanks are due to  Tomasz Mamos  for his help and assistance in performing the phylogenetic analyses and EBSP.
8%  [23]. However, the relevance of these findings will be assessed only when PFS and/or overall survival data are available. There are several unanswered questions about which patients with breast cancer are most likely to benefit from one or another form of anti-HER targeted therapy and which type of determination methodology is most appropriate. DNA-based, RNA-based, and protein-based assays have been developed to determine the HER status of breast tumors. To date, exploitation of the overexpression of HER2 is part of the management of a breast cancer patient whereas EGFR, HER3, and HER4 determinations are still exploratory and not used in clinical practice. As a matter of fact, to be eligible for anti-HER therapy such as trastuzumab, specimens have to be HER2-positive as determined by immunohistochemistry (IHC) analysis or harbor HER2/neu gene amplification by fluorescence in situ hybridization (FISH).
Interpretation of membrane staining can be optimized using quantitative image analysis such as the automated quantitative analysis (AQUA) system  [33]. AQUA is a fluorescence IHC-based method that provides objective and continuous protein expression scores for tissues by using automated fluorescence microscopy and advanced image analysis algorithms. It is important to note, however, that there are as yet no clinical data related to AQUA's predictive ability. Other software applications include Aperio Scanscope (Aperio Technologies; Vista, CA, USA), Definiens (Carlsbad, CA, USA) and Vysis AutoVysion (Abbott Molecular, Des Plaines, IL, USA) among others. These applications can reduce the subjectivity of a traditional scoring system and provide a more reproducible protein expression score  [34, 35].
Enzyme-linked immunosorbent assay HER2 receptor protein extracellular domain (ECD, p105) is released into the circulation after cleavage by matrix metalloproteinases and its levels can be measured in the serum using an enzyme-linked immunosorbent assay approved by the FDA (Siemens Healthcare, Erlangen, Germany). Elevated levels of serum HER2 ECD have been shown to be both prognostic and predictive of response to trastuzumab in HER2-positive tumors  [36, 37]. Serum ECD values have been suggested, therefore, as an alternative technique for determining HER2 status, although available results are controversial. First, not all patients with HER2-positive tumors appear to have elevated serum ECD values and patients with HER2-negative tumors can also have elevated ECD values. Second, the reported data come from studies including a limited number of patients, thus making current evidences still insufficient to consider basing treatment decisions on ECD levels in routine clinical practice.
A large meta-analysis study  [38]  has combined the data of four trastuzumab trials in metastatic breast cancer and showed that, from the combined dataset (N = 322 patients), there was no correlation between baseline ECD value and tumor response. ECD values decreased upon initiation of combination therapy irrespective of treatment and tumor response. Furthermore, disease progression was not reliably predicted by an increase in ECD levels. Therefore, the use of ECD values in treatment decision making was not recommended.
Introduction More than 3 decades ago, the CERN S p pS data provided important results in the energy range √ s = 200 -900 GeV implemented rapidly in cosmic ray simulation, especially with the potential of UA5, UA4, UA2, UA1 measurements  [1]. This energy range was extended near five years later when the CDF detector started operating at Fermi-lab up to √ s = 1. 8 TeV equivalent to an energy exceeding 1. 6 • 10 15 eV close to the cosmic ray knee at 3 • 10 15 eV  [2]. Measurements of the charged particles density of pseudorapidity, η, were among the earliest observations made at S p pS, together with the average central pseudorapidity density ρ(0). Simultaneously, single diffractive, double diffractive cross sections were estimated as well as the transverse momentum, p t , distribution of charged secondaries.
Like in the International Court of Justice of the Hague, the recent results of very energetic colliders select the most reliable models and dictate further improvements. Unfortunately, in the interpretation of cosmic ray data, the approach has a limit due to the difficulties of measurement in the very forward region of rapidity and the determination of the properties of the leading cluster. In other words, the role of the pilot particle fundamental in hadronic cosmic ray cascades remains dependent on adequate assumptions. All the models have been extended to p-A and A-A collisions in order to describe the collision of cosmic ray hadrons with air constituents and the comparison with LHC data becomes also possible. Conversely, there exist also models elaborated directly for collisions of heavy nuclei which can be tuned to describe p-p collisions; this is the case, for instance, of the non-equilibrium statistical relativistic diffusion model (RDM) based on a Fokker-Planck type transport equation  [5].
Normalized pseudorapidity density and semi inclusive data An interesting property of the normalized central density of pseudorapidity has been observed also in the semi inclusive data in colliders and accelerators  [7]. A new kind of empirical scaling has been pointed out in the relation density. The KNO variable z = n n /n ch is the normalized multiplicity. The general dependence on z is shown in Fig. 3. Following our adjustment, we propose for f (z) the numerical representation: ρ n (0)/ρ(0) = f (z) instead of f (z, s), i. ζ = f (z) = 0. 11499 z 2 + 1. 0231 z -0. 1319. (4) This new kind of scaling is not yet confirmed in LHC data as the semi-inclusive measurements are performed only in small windows of pseudorapidity; nevertheless, the important fluctuations of those reduced distributions suggest a similar amplitude of the KNO violation.
ACKNOWLEDGEMENTS The authors wish to thank for the comments on an earlier draft of the contribution received at the workshop on 'Ideas,  Political Power  and  Public Policy', Snekkersten, Denmark ,  9 -10 June 2014 , along with the input from three anonymous reviewers. The research presented in the contribution was funded by the  H2020  grant 'European Legitimacy in Governing through  Hard Times  (# 649456 -ENLIGHTEN ).