dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements: We would like to thank  Silvano Romano  for encouraging us to carry out this investigation. Thanks to  Robin de Regt  for help with gathering data in the early part of this project. This work was supported by the  EU   FP7  Projects No. 269139 ,  "Dynamics  and  Cooperative Phenomena in Complex Physical and Biological Media ," No. 295302 , " Statistical Physics in Diverse Realizations " and No. 612707 , " Dynamics of and in Complex Systems ," and by the  Doctoral College for the Statistical Physics of Complex Systems, Leipzig-Lorraine-Lviv-Coventry, Franco-German University. PMC is supported by a  European Research Council  (No. 295663 )  Advanced Investigator grant  to  R. I. M. Dunbar.
Apparatus Eye movements were recorded with the video-based SR Research EyeLink II system with a high spatial resolution (noise < 0. 01°) at a sampling rate of 500 Hz. Stimuli were displayed on a 22" CRT monitor at a resolution of 1024 × 768 and a refresh rate of 110 Hz. A viewing distance of 60 cm was assured by use of a chin rest. The experimental software controlling stimulus display and response collection was implemented in MATLAB (The Mathworks, Inc. ), using the Psychophysics Toolbox extensions  [41]  as well as the Eyelink Toolbox extensions  [42].
Data from the right eye were analyzed. If not stated otherwise, the data reported are based on the first (primary) saccade of each trial, regardless of whether subsequent saccades occurred. Data for left and right target presentation were pooled; presentation side had no significant effect. For statistical analyses means were calculated for each subject, and these were then averaged across subjects. Analyses of variance (ANOVA) or t tests were run on the means obtained for each subject in each condition. For repeated-measures ANOVAs we report Greenhouse-Geisser corrected degrees of freedom and p-values in cases where Mauchly's test indicated that sphericity was violated.
Results Mean accuracy in the discrimination task was 90. 25%. Considering a window of 250 ms following online saccade onset detection, trials containing missing data were removed (0. 62%). The saccades detected online were validated by comparing them with saccades detected offline. Trials were excluded from analysis if the latency of the online detected saccade was considerably shorter (30 ms or more) than the latency of the offline detected saccade (11. 4%). One additional trial was excluded because the first saccade was classified as an anticipatory saccade. To test the SRE hypothesis, mean landing position was computed for each eccentricity in the proximal (  The results did not support the SRE hypothesis. In a given block, there was no overshoot for small eccentricities. Across the two blocks, mean saccadic endpoints (landing positions) appeared to be precise for eccentricities smaller or equal to 7.
X-ray Measurements and Spherical Structure-Refinements (IAM-Independent-Atom Models) The crystal and molecular structures of 1-2 were determined by single-crystal X-ray diffraction. Data collection was carried out at 100K with an Agilent SuperNova diffractometer with an Atlas detector (Agilent Technologies, Yarnton, Oxfordshire, UK) (1) and an Oxford Xcalibur diffractometer with a Sapphire3 detector (Oxford Diffraction, Abingdon, UK) (2), both using MoK α radiation and ω scan. The data reduction and multi-scan absorption correction were performed by CrysAlis PRO  [40]  and CrysAlis RED  [41]  software for 1 and 2, respectively. Both structures were solved by direct methods and refined by full-matrix least-square procedures on F 2 using the SHELXL-2013  [42]  program package implemented in WinGX  [43].
Acknowledgments: Authors acknowledge the financial support from the  Polish National Science Centre  (grant No. 6740/B/P01/2011/40 ; reg. No. N N405 674040 ) and the statute fund from  Medical University of Lodz  ( 503/3-016-02/503-31-001 , and  502-03/3-016-02/502-34-062 ). The open access publishing fee was covered by the  Faculty of Pharmacy Dean's (Medical University of Lodz ) fund No  503/3-016-02/503-36-001. We thank  Agnieszka Zdolska  for her help in the preparation of the compounds. Collecting the X-ray data was ordered to be done in  Department of Physics of the Crystals, at the University of Silesia in Katowice, Poland. Collecting NMR spectra were recorded to in  Laboratory of Molecular Spectroscopy, Faculty of Chemistry, University of Lodz, Poland.
Standard extra and intra oral examination was performed. Intra oral examination started from the first to the fourth quadrant; data about number of teeth with caries, extracted teeth, filled teeth and use of partial and full dentures was collected. To assess oral hygiene, Plaque Index (PI) according to Silness & Löe  [23]  was used. Values from 0 -no plaque to 3 -a thick layer of plaque biofilm filling the interdental area were recorded for each tooth excluding third molars. For each participant the mean value for all teeth was used in the analysis. Participants who had mean PI > 2. 5 were classified as having poor oral hygiene. Pocket depth (PD) was measured for each tooth excluding third molars. Probing was performed on six surfaces of each tooth using periodontal Hu--Friedy probe type UNS-PCP 15.
The relation between lifestyle factors and the number of remaining teeth was assessed using multivariate linear regression. The odds of being edentulous, having poor FD, having PD ≥ 6 mm, having PI ≥ 2. 5 (3rd quartile value) was assessed using multivariate logistic regression. Statistical significance was accepted at the level of p < 0. 05. All calculations were performed in SPSS Software, IBM, Armonk, NY.
A simplified and easy to use but comprehensive method tool, based on Excel, has been prepared using a database of different thermal components (chillers, collectors, heat storages, …) as well as PV-system components (photovoltaic panels, inverter, storages, …). The tool is developed with the following characteristics: easy-to-use: it can be used both by LCA practitioners and non-professional users; easy to update: it can easily be expanded with the life cycle data of new components or updated with new life cycle data for the existing components; applicable to different geographic contexts and devoted to perform parametric analyses for SHC systems. Figure  3  shows the main menu of the tool. The data base also embeds the impacts of electricity use in 25 different countries. This allows additionally a comparison of both technological approaches.
Simplified design tool used as a reference calculation tool: "Design facilitator" The PISTACHE tool  [4]  is a simple to use pre-sizing and evaluation tool for solar cooling, heating and domestic hot water systems. It is available on the websites of the Task 48 and of the company TECSOL. The software-tool calculates energy balances and performance indicators based on input data for a pre-selected hydraulic scheme. The user has to provide local information of the planned installation like meteorological data of the site, cooling and heating loads as well as the domestic hot water demand of the building. An automatically provided short interpretation of the annual energy results gives fast support for the investor and planner. It provides as well monthly and annual energy balance of a pre-defined configuration.
In the second part of the report some of existing error detection systems and methods are presented: e. g. , the concept of METHODIQA is based on online analysis of operation data and the calculation of specific benchmarks. These values are evaluated using intelligent algorithms to identify malfunctions or unfavorable operating conditions. Another described self-detection system uses a MATLAB routine to compare performance indicators which are derived from the system monitoring and online simulation. In general, automated fault detection uses more and more self-learning procedures which help to reduce significantly the external evaluation effort of the system operator.
As previously shown  [18]  MDA-MB-231 wt expresses neutral globosides Gb3 and Gb4 and monosialylated gangliosides, mainly GM3 (Figure  3a ). The precursor lactosylceramide (LacCer) was also detected, as well as a monosialoganglioside at m/z 1933, which was confirmed to correspond to GM1b by matrix assisted laser desorption-ionization time-of-flight (MALDI-TOF)/TOF fragmentation analysis (data not shown). Two ceramide isoforms are commonly expressed in human tissues due to the substitution of the sphingosine moiety by palmitic acid C16:0 (Cer*) or lignoceric acid C24:0 (Cer**). As shown in Figure  3b , the composition in GSLs of BrM2 cells was similar to wt cells and no expression of GD1α was detected as indicated by the absence of signal at m/z 2293. 9, which was identified in MDA-MB-231 green fluorescent protein positive (GFP+) cell population (Figure  3c ) by MALDI-TOF/TOF fragmentation analysis (Figure  S1 ).
The precursor lactosylceramide (LacCer) was also detected, as well as a monosialoganglioside at m/z 1933, which was confirmed to correspond to G M1b by matrix assisted laser desorption-ionization time-of-flight (MALDI-TOF)/TOF fragmentation analysis (data not shown). Two ceramide isoforms are commonly expressed in human tissues due to the substitution of the sphingosine moiety by palmitic acid C16:0 (Cer*) or lignoceric acid C24:0 (Cer**). As shown in Figure  3b , the composition in GSLs of BrM2 cells was similar to wt cells and no expression of G D1α was detected as indicated by the absence of signal at m/z 2293. 9, which was identified in MDA-MB-231 green fluorescent protein positive (GFP+) cell population (Figure  3c ) by MALDI-TOF/TOF fragmentation analysis (Figure  S1 ).
The quantification of adherent and transmigrated cancer cells was done manually on the total surface of each filter under a Leica DMR fluorescence microscope (Leica Microsystem, Wetzlar, Germany). The number of adherent or transmigrated MDA-MB-231was set to 100%. All results were expressed as the mean ˘standard error of the mean (SEM) from two or more independent experiments. Statistical significance was assessed by t-test. All statistical analyses were performed using GraphPad Prism version 5. 0 for Windows (GraphPad Software, San Diego, CA, USA).
mRNA Extraction and PCR Analysis Cancer cells were lysed with 500 µL of RLT lysis buffer (Qiagen, Les Ullis, France). mRNA was purified using the RNeasy total RNA extraction kit (Qiagen), following the manufacturer's instructions. After this step, the mRNA's purity and concentration were assessed by measuring the absorbance at 260, 280, and 320 nm using the Take 3 microplate reader protocol (Synergy™ H1, BioTek Instruments, Colmar, France). For each condition, cDNAs were obtained from 0. 5 µg of mRNA using iScript™ Reverse Transcription Supermix (BioRad, Marnes-la-Coquette, France), according to the manufacturer's instructions. Real-time PCR experiments were performed using the Sso Fast EvaGreen Master Mix kit (BioRad). Primers for ACTB (sense: 5 1 -ggagcacagagcctcgcctt-3 1 , antisense: 5 1 -acatgccggagccgttgtcg-3 1 ), ST6GALNAC5 (sense: 5 1 -ggatcccaatcacccttcag-3 1 , antisense: 5 1 -tagcaagtgattctggtttcca-3 1 ), COX2 (sense: 5 1 -tccaccaacttacaatgctgac-3 1 , antisense: 5 1 -cacaggaggaagggctctagta-3 1 ) and HBEGF (sense: 5 1 -ggttaccatggagagaggtgtc-3 1 , antisense: 5 1 -gaccagcagacagacagatgac-3 1 ) were designed using Primer 3 software.
The mixtures (2 µL) were then spotted on the target plate and air dried. MS survey data comprises a total of 50 sub-spectra of 3000 laser shots. Peaks observed in the MS spectra were selected for further MS/MS. Collision-induced dissociation (CID) MS/MS data comprises a total of 100 sub-spectra of 3000 laser shots. Two or more spectra can be combined post-acquisition with mass tolerance set at 0. 1 Da to improve the signal-to-noise (S/N) ratio. The potential difference.
Acknowledgments: This work was supported by the  University of Lille , the  Artois University , the  Centre National de la Recherche Scientifique (CNRS) , the  comité du Pas-de-Calais de La Ligue contre le Cancer and La Région Nord-Pas-de-Calais (fellowship Drolez A and Vandenhaute E). We thank the  Joan Massagué  from the  Memorial Sloan Kettering Cancer Center  for providing us the MDA-MB-231BrM2 cell line.
The genome sequencing was performed using the next-generation sequencing SOLiD platform, using a fragment library. The predicted genome coverage was approximately 6,000ϫ, based on C. ulcerans genomes available in GenBank (http://www. ncbi. nlm. nih. gov/genbank/). The de novo assembly strategy was performed using Velvet software version 1. 2. 10 (7), generating 28 contigs with 2,497,845 bp. The contigs were submitted to Gen-Bank for automatic annotation. The genome has 2,059 coding sequences, 12 rRNA genes, 46 tRNA genes, 150 pseudogenes, 1 clustered regularly interspaced short palindromic repeat (CRISPR) array, and a GϩC content of 53. 50%. This genome is part of further studies of comparative genomics, pathogenicity, and vaccine and drug targets of the species. Nucleotide sequence accession numbers.
Cost Allocation Scenarios for Each Scenario The next step of the analysis consisted of quantifying the distribution of the cost among players for each scenario (we assumed the same three players A, B, and C as in the game theory setting to allow a comparison of the results). The data provided to estimate the cost allocation associated with each scenario have been collected from previous local studies in the area for: water withdrawals, the potential of saving efforts, and the allocation of water among the various users  [Vernier and Rinaudo, 2012; Rinaudo et al. , 2016b].
Acknowledgments The study has been partially supported by the  IMPADAPT  project ( CGL2013-48424-C2-1-R ) from the  Spanish ministry MINECO (Ministerio de Econom ıa y Competitividad)  with  European FEDER  funds. The first author is supported by a grant from the  University Lecturer Training Program  ( FPU12/03803 ) of the  Ministry of Education, Culture and Sports of Spain. The second author is financially supported by  BRGM 's research program 30 (environmental and risk economics). Readers interested in the data can request those by e-mail to  Corentin Girard , cogimar@upv. es. Water Resources Research 10. 1002/2016WR018757.
Funding This work was funded by the  Spanish Ministry of Economy and Competitiveness , through the  Severo Ochoa Program for Centres of Excellence in R+D+I  ( SEV-2012-0262 ), through the  I+D+i National Plan  (Project  CGL2013-47674-P ) and by the  7th Framework Programme  ( FP7 2007-2013 ) of the  European Commission  through a  Marie Curie Intra-European Fellowship for Career Development  ( PIEF-GA-2011-299747 ). M. I. Sanchez was supported by a Ramon y Cajal postdoctoral contract from the  Spanish Ministry of Science and Innovation (MICINN). M. Martı ´nez-Haro  was supported by a postdoctoral contract funded by the  Junta de Comunidades de Castilla-La Mancha  ( POST 2014/7780 ). Currently, MMH holds a  Juan de la Cierva  research contract (IJCI-2014-20171) awarded by the  Spanish Ministry of Science and Innovation-European Social Fund.
Grant Disclosures The following grant information was disclosed by the authors: Centres of Excellence in R+D+I:  SEV-2012-0262. I+D+i National Plan :  CGL2013-47674-P. 7th Framework Programme: FP7 2007-2013. Marie Curie Intra-European Fellowship for Career Development :  PIEF-GA-2011-299747. Junta de Comunidades de Castilla-La Mancha :  POST 2014/7780. Juan de la Cierva  research contract:  IJCI-2014-20171.
HEARTEN adherence risk module predicts not only medication adherence of the patient, but also of the global adherence of the patient. 2. Materials and methods: Two classification problems are addressed: (i) if the patient is global adherent or not and (ii) if the patient is medication adherent or not. To achieve this, a three stages method is proposed, as depicted in Fig. 1. A detailed description of these stages follows. The first stage focuses on the removal of features that present high percentage of missing values. The second stage aims to identify predictors of adherence/non-adherence of the patients. The third stage is the core stage of the proposed method, since it informs the medical experts if the patient is adherent or not. The features that are recorded for each patient can be grouped to the following six categories: (i) general information (age, gender and caregiver), (ii) allergies and (iii) medical condition, which includes information regarding the KILLIP classification, the NYHA class, the smoking habit, the alcoholism habit, of the patient, as well as, the presence or not of comorbidities, (iv) drugs (the active substances, the dose and the frequency of intake), (v) biological data related with HF disease and (vi) clinical examinations (left bundle branch block or intraventricular delay, left ventricular ejection fraction).
Totally, 100 features are recorded for each patient that according to the literature are correlated, positively or negatively, with patient adherence. These six categories of features are related to the third (patient health condition including NYHA class and the presence of other comorbidities), the fourth (suggested therapy) and the sixth (patient-related factors) group of factors affecting adherence, as mentioned in Section 1. Data about social and economic dimensions, health care system and knowledge of patient about the disease will be collected during the prospective phase of the HEARTEN project.
RFs belong to the category of ensemble classifiers since it is composed of many decision trees. Each tree votes for one of the classes. The final classification of a sample derives from a majority voting procedure. More specifically, the class that collects the majority of tree votes is the predicted class of the classified sample. Each tree of the forest is built to the maximum size without pruning, using a new subset of samples selected from the dataset. At each node of the tree, a subset of features is employed. The square root of the number of features of the dataset equals to the cardinality of this subset of features. The number of selected features remains constant throughout the construction of the forest. To determine the best split attribute for each node of the tree, the Gini index is employed.
The data is transformed linearly into the new features. Through this new dataset, a tree classifier is trained. For the construction of the trees, the J48 algorithm is used. SVM is a supervised learning method which is utilised for classification. SVM is based on the definition of an optimal hyperplane, which separates the training data between the classes. The hyperplane is defined so that simultaneously the expected risk is minimised and the distance of the data points from the linear decision boundary is maximised. This to be achieved, support vectors are used. The computations in the feature space are simplified through the kernel function that is used. The kernel function must satisfy the Mercer's conditions. RBF network constructs a normalised Gaussian RBF network. RBF network consists of: (i) an input vector, (ii) a layer of RBF neurons and (iii) an output layer with one node per category or class of data.
According to this assumption, the probability of each attribute belonging to a given class value is independent of all other attributes. MLP is a network that uses backpropagation to classify instances. It is organised in the following layers: (i) input layer, (ii) hidden layers and (iii) output layer. Each layer is constructed by a number of nodes, which have an activation function. The samples of the dataset are presented to the network through the input layer, the sample is processed to the hidden layers and the decision is given to the output layer. Simple CART is a non-parametric binary decision tree learning technique. Measure of impurity depends on the nature of the features (categorical or continuous). Stopping criteria may concern the structure of the node (size, purity, values of predictors etc.
) and the structure of the tree (depth). Once the growing process is completed, pruning process starts. CART algorithm handles missing values by utilising 'fractional instances' method or surrogate split method. The implementation of the classifiers, provided by the Weka software (http://www. cs. waikato. ac. nz/ml/weka/index. html), is employed. More specifically, version Weka 3. 6. 13 of the software is utilised. The default values of the parameters have been used for every one of the above-mentioned classifiers and no optimisation procedure has been applied.
Results: The proposed method is evaluated using a dataset of 90 patients. For each patient 100 features, described in Section 2. 1, are recorded. The feature vector of each patient is augmented by two features expressing the annotation of the patient as medication adherent (med_adh) and global adherent (adh), provided by the experts. The proposed method is applied two times in order the two classification problems to be addressed. The first problem aims to classify a patient as global adherent or not, whereas the second aims to classify a patient as medication adherent or not. For each classification problem, two sub-datasets N×M are created. For the first classification problem, where the target output c of the classification is global adherent or not (c = adh), the dimensions of the two sub-datasets are N = 90 instances and M = 81 features (80 features plus med_adh) (1_a) and N = 90 instances M = 80 features (1_b).
Accordingly, in the second classification problem, where c = med_adh, the two datasets include N = 90 instances and M = 81 (80 features plus the adh) (2_a) and N = 90 instances and M = 80 features (2_b). The motivation for the division of the dataset to 1_a and 1_b sub-datasets is to study if the inclusion of information regarding medication (non)adherence of the patient affects the prediction of global adherence, while through the creation of the 2_a and 2_b sub-datasets we aim to examine if the fact that a patient is global adherent or not affects the prediction of medication adherence. Each one of the four sub-datasets are given as input to the proposed method, described in Section 2, and the results are reported in Table  1. The combination of FS measure and classifier that provide the best accuracy (Acc) are reported only.
Finally, classification in combination with re-sampling, using the SMOTE algorithm, Stage 3 is applied. For the two sub-datasets of the first classification problem (1_a and 1_b), the value of the parameter that specifies the percentage of instances that are created by SMOTE is set equal to 100, while for the sub-datasets of the second classification problem (2_a and 2_b) is set equal to 300. The values 100 and 300 are selected in order the number of instances that belong to each class to become balanced. For all the sub-datasets, the number of nearest neighbours that are used is 5. This is a default parameter of the SMOTE algorithm and it is not optimised in the context of the current work. The results in terms of Acc, positive predictive value (PPV), sensitivity (Sens), specificity (Spec), area under curve (AUC) and F-measure are reported in Table  4.
Furthermore, it provides an estimation if the HF patient is going to be adherent or not, regarding all aspects of patient management such as medication, nutrition and physical activity. The method is evaluated stage by stage on a dataset of 90 patients. This dataset is divided into four sub-datasets (1_a, 1_b, 2_a, 2_b), two for each classification problem, and the obtained Acc is 82, 76, 91, 78%, respectively. The results indicate that the information regarding medication adherence affects the prediction of global adherence (case 1_a) and vice versa (case 2_a). The comparison of the proposed method with the one reported in the literature (Son et al. [17] ) is presented in Table  6. The current study provides equal or greater results in terms of Acc, PPV, Sens for three out of four cases that are examined, while Spec of the proposed method is lower compared with the one reported by Son et al.