dataesr/scientific-paragraphs-categorization huggingface dataset:
Molecular methods Genomic DNA was prepared from scarped colonies in 200 μl of 1xTE buffer (10mM Tris-HCl pH: 8. 0, 1mM EDTA pH: 8. 0) followed by heat inactivation at 85°for 30 min  [20]. Spoligotyping was carried out for 168 isolates, using the commercially available membranes (Ocimum Biosolutions, Hyderabad, India)  [5]. Standard 24-locus based MIRU-VNTR typing was performed for 75 isolates using the available commercial kit (Genoscreen, Lille, France) and 16 capillary ABI 3730 genetic analyzer (Applied Biosystems, CA, 78 USA) as described previously  [4]. MIRU-VNTR alleles were determined by using Genemapper V-4. 0 (Applied Biosystems, CA,79 USA) and data were compiled by using MIRU-VNTR Data Manager software (Genoscreen, Lille, France).
Molecular data analysis Spoligotyping results were converted into octal codes and entered in SITVIT database  [21]  for analysis. Major spoligotypes families were assigned according to signatures provided in the database. Distinction between evolutionary, ancient, and modern lineages of tubercle bacilli was made as described  [22] [23] [24] [25] [26] [27]. MTBC genetic lineages were predicted using online tools available from MIRU-VNTRplus website (www. miruvntrplus. org [28] ) according to the previously described strategy combining best-match and phylogenetic based analysis  [29]  and by using information in the SITVIT database (fr:8081/SITVIT ONLINE/indexjsp). Molecular clustering of the isolates was determined by constructing a dendogram based on spoligotyping and MIR-U-VNTR data. A strain cluster was defined as two or more isolates sharing completely identical fingerprints based on both methods.
Spoligotyping data analysis Analysis of the spoligotyping data was done by assigning shared international type (SIT) numbers and genotypic spoligotype family designations in comparison with SpolDB4  [21]  and its updated SITVIT version  [22]  (S1 Table ). The global sample containing 10/168 (6. 0%) isolates that could not be assigned into well-determined designations in both, and as such were labeled as ''unknown". In contrast, the remaining, 158 isolates belonged to 39 classified SITs in the international SITVIT database. A total of 137/168 (81. 54%) clinical isolates were clustered 18 clusters containing 2-50 isolates per cluster while 31 were unique (21 with an assigned SIT, and 10 unknown from SITVIT). The major spoligotype families observed ranked as follows: Latin-American & Mediterranean or LAM, 81/168 (48.
Molecular cluster analysis A subset of MTBC isolates (75/168) was subjected to 24-locus MIRU-VNTR typing. These isolates were selected to cover the different cities included in the study and different already established spoligotype families; they were originated from both pulmonary and extrapulmonary TB patients, with different drug susceptibility profiles (pansusceptible, polyresistant and MDR) (Table  2 ). Among the 75 isolates, molecular cluster analysis based on 24-locus MIRU-VNTR genotypes identified 69 distinct genotypes, only including 5 clusters comprising from 2 (n = 4) to 3 isolates (n = 1) each (total of 11 isolates) and 64 unique types (Fig  1  and Table  2 ). Out of these unique types, 5 could not be assigned into well-determined genotypic lineage in the MIRU-VNTRplus database, SpolDB4 and SITVIT database and as such were labeled as "unknown".
Only a few outliers were detected, for which samples were unavailable for repeat experiments in order to see if these few discordant groupings reflected some homoplasy linked to spoligotyping or MIRU-VNTR data  [36] , or technical errors. In this sample collection, two different isolates were also obtained from one patient with both pulmonary and extrapulmonary TB. Both spoligotyping and 24-locus MIR-U-VNTR typing showed that the strain isolated from sputum defined as ST273/LAM9 and MLVA MTBC Unk-488 differed from the one isolated from pleural liquid clinical sample, defined as ST784/T2 and MLVA MTBC Unk-85. This concordant result obtained by independent typing methods suggests a case of mixed infection with compartmentalization of different strains in distinct body sites  [37]. Considering the full set of spoligotyping data obtained for the 168 study isolates, our results confirm that MTBC isolates in our country are essentially limited to evolutionary modern principle genetic group (PGG)2/3 strains (namely LAM, Haarlem, and T), as found in previous reports and different patient populations  [16, 17].
Overall, when our results are compared with those of these previous reports, it seems that MTBC population structure in Morocco is highly stable, with almost same spoligotype distributions from 2002 to 2012, and highly homogeneous. Strikingly, each of the 3 predominant LAM, Haarlem and T families was characterized by one or two predominant SITs (i. e. SIT42 for LAM, SIT50 for Haarlem and SIT53 for T), almost always representing the prototype of each spoligotype family with large geographical distribution described in different databases  [21, 22]. It can be speculated that this predominance of a few SITs, especially that of LAM family, reflects some founder effect linked to the introduction of the corresponding MTBC clonal branches in the region. In contrast, although we found a total of 49 profiles identified among the 168 isolates, we did not find spoligotypes with strong local phylogeographical specificity (Table  2 ).
Acknowledgments We would like to thank all patients and HC for their participation. We are sincerely grateful to the clinicians and nurses of the different prefectures ( Marrakech, Rabat, Temara, Sale, Fes, Oujda, Tanger and Casablanca ) for their contribution to the clinical examinations. The collaboration with  Institut Pasteur de Lille France  was financed through the  Euro Mediterranean Network against Tuberculosis ,  European Commission ,  7th Framework program  (EUMED-NETvsTB;http://www. eumednet-tb. org/index. html).
The work performed was funded by  Académie Hassan II des Sciences  (grant number  1169 ). The collaboration with  Institut Pasteur de Lille France  was financed through the  Euro Mediterranean Network against Tuberculosis ,  European Commission ,  7th Framework program  (EUMEDNETvsTB;http://www. eumednet-tb. org/index. html). However, the funders had no role in study design, data collection and analysis, decision to database.
In this paper we present a collaborative data science project bringing together experts from the fields of physical and information sciences to tackle to challenges and opportunities of analysing large and rich datasets obtained from the monitoring of building integrated PV systems operating in the built environment. We present data mining analysis techniques to classify data according to environmental conditions and system performance, to distinguish between nominal and anomalous behaviour, and to identify instrumentation faults. These methods were implemented using data from the RESSOURCES project to construct evaluate the performance of a PV envelope, and to validate a simplified physical model to predict thermal and aerodynamic behaviour.
N. W wall of a three-storey, open-plan office building. Measuring 7. 4 m in height, 4. 0 m in width, and with an airgap of 60-80cm, the prototype covers the first two floors of the building. The PV components are divided into a stack of three separate arrays, hereafter referred to as 'blocs', connected to a constant load, with a total rated power of 1. 2 kW. The data used in this paper span a three month period, 21/06/2012 -20/09/2012. Previous results  [3]  have shown that despite the level of geometrical complexity of the HBS prototype and the variability of the environment, the behaviour of the system follows regular and periodic trends over timescales of one day and one year. The data retrieved from this and other PV installations contain a wealth of information that could be further exploited to improve the understanding of the technology, but novel methods are needed to analyze sizeable datasets comprising a large number of parameters, a multiplicity of spatial and temporal scales, and a level of instrumentation that is typically too coarse to reveal the detailed interaction of phenomena such as wind and shading effects.
Data mining methods The analysis was developed with the aid of the open source software Knime  [4]. A workflow was first assembled to manage the interface with the MySQL database repository, and handle the tasks of spatial and temporal aggregation, and filtering, that were previously undertaken using Scilab. Several data mining techniques were then appended to this analysis chain, and in particular the k-means clustering algorithm. Rarely used in scientific research of solar systems in urban environments, these methods have been demonstrated effective in other domains such as life sciences research (gene expression studies) for many years  [5]. The k-means method is one of the oldest and most widely used clustering routines  [6, 7]. The algorithm regroups data into k partitions or clusters in n dimensions, which for the current application correspond to indicators of system behavior or environmental factors.
Cluster centroids are determined (in our case using a Euclidean metric), and adopted as a new set of cluster centres. The clustering is then repeated and this process continues until cluster centres have stabilized. The obtained clusters provide additional information for a data analysis by regrouping similar data together, which may therefore help to distinguish periods of nominal behavior and anomalies. A major advantage of the k-means algorithm is its speed, which scales well with the size of dataset and number of dimensions. Among its shortcomings are the need to choose the number of clusters and set of dimensions by hand, and to a lesser degree, a certain sensitivity to the starting point of the calculation. To overcome these weaknesses in the present analysis, the k-means tool was used multiple times for different configurations before choosing the best set of results.
Clustering according to electrical performance and solar radiation The present analysis addresses the relationship between electrical power and incident radiation. Previous studies of correlation plots revealed multiple structures, indicating a superposition of different behavior in the dataset  [3]. The k-means algorithm was used to isolate these features. The algorithm was found to provide a good partitioning of data for k=6 clusters defined by the measured radiation intensities (total façade, total horizontal, and direct normal) and the performance ratio of each bloc, which is a measure of yield relative to incident radiation, both normalized to reference values, as defined by equation 1. P dc P c G i G ref (1) In figure  2 , the resulting clusters are shown for the three blocs, with marker styles indicating the cluster association common between the three graphs.
In addition, a horizontal banding is clearly visible, indicating power losses correlated with time of day, which are caused by fixed obstacles in the vicinity of the façade. The carpet plot visualisation of clusters shows a clear relationship with time of day for most of the clusters, which therefore further adds to the description of these clusters. Night time is completely accounted for by cluster 6. Similarly, cluster 4 regroups dawn and dusk, early morning and early evening for most of the days in the sample. Comparing the cluster associations to the other carpet plots, it is clear that cluster 3 is attributed cloudy periods. Moreover, days with partial cloud cover are divided into several clusters, which may indicate the presence of various competing phenomena during such days. The rest of the dataset is primarily attributed to one of three clusters, which appear correlated to the horizontal banding observed in the electrical performance carpet plot.
During the measurements, the transducer was maintained in fixed position and samples were displaced with a XY displacement system, type Newmark-Newmark Systems Inc. (Santa Margarita, CA, USA) that ensures the raster scanning in plane with ±10 µm precision and rotation with ± 2′′. The measurement system is commanded via PC through an RS232 interface (National Instruments, Mopac Expwy, Austin, TX, USA) for displacement system and IEEE 488. 2 for Network/Spectrum/Impedance Analyzer (National Instruments, Mopac Expwy, Austin, TX, USA). The data acquisition and storage are made by software developed in Matlab 2012b (The MathWorks, Inc. , Natick, MA, USA). The e. m. f. induced in the reception coil of the measurement system is the average of 10 measurements at the same point in order to reduce the effects of the white noise, the bandwidth of the analyzer being set to 10 Hz, also to diminish the noise level.
In the case of an MSG made from silver strip having 14 µm thickness, the width of strips where xc = 1. 2 mm and the width of slits is xd = 0. 8 mm is analyzed, considering that the wavelength of incident field is λ = 0. 6 m (corresponding to a 500 MHz frequency). A region of 16 × 16 mm 2 from MSG has been scanned with 0. 1-mm steps in both directions. The scanning along the x direction was done to correspond with a few periods of gratings, x0 = xc + xd. The working frequency was 476 MHz. For MSG excitation with EM field TMz polarized, the simulation was performed using XFDTD 6. 3 software produced by REMCOM (State College, PA, USA)  [86]. According to Ref. [79] , the value of dielectric permittivity of silver is εm = -48. 8 + j•3. 16. Figure  6  shows the dependency of e. m. f. amplitude induced in the reception coil of a sensor on the scanning of the MSG taken into study, the image showing that this type of sensor correctly relies on conductive strips with 14 µm thickness and eventual non-adherence to support and/or interruptions.
One cell of woven carbon fiber has been designed in the TexGenTextile Geometric modeler software (University of Nottingham, Nottingham, UK) (Figure  7a ) and exported in CAD format in order to be used in FDTD software (XFDTD REMCOM, State College, PA, USA). The 5 × 5 tows were represented in different colors in order to easily follow their intersection when woven. The cell dimension in FDTD simulation is 0. 04 mm, and the grid size is 146 × 146 × 45 cells. The perfectly matched layer boundary condition was applied at the grid boundaries. The result of the simulation presented in Figure  7b  is a snapshot from a field sequence, showing the Hy field progress at a particular slice of the geometry. In this case, the role of conductive strips is taken by carbon fibers, which act as MSG  [71] ; the apparition of evanescent waves can also be emphasized.
In the current study, the hierarchical structure of DSM-5 traits will be explored. Internalization and Externalization are expected at the top of the hierarchy with the pathological five-factor model at lower levels. In the current study, the French version of the self-report form of the Personality Inventory for DSM-5 (PID-5) was administered to French-speaking adults from a European community sample encompassing subsamples from Belgium, France and Switzerland. In the data analysis, an attempt was first made to test the assumption of unidimensionality of both the facet and the domain scales. We then tried to replicate the initial five-domain structure of the PID-5  [6]. Finally, Goldberg's approach was used to replicate the hierarchical structure of the traits, delineating a model with higher-order dimensions of Internalizing and Externalizing under which three to five broad dimensions correspond to fundamental dimensions of personality functioning  [7, 16].
Method Sample At the outset of the study, 2,648 adults were willing to participate. Of these, 2,532 (25. 9% men) provided complete data (95. 62%): 1,593 came from the French-speaking part of Belgium (62. 9%), 536 were French-speaking Swiss (21. 2%), and 403 were French (15. 9%). Their ages ranged from 18 to 85 years (M = 27. 22, sd = 13. 28). Statistical analyses were computed with the complete data sets of these 2,532 participants. For the French sample, the participants were psychology undergraduate volunteers from the University of Poitiers. The questionnaire was completed during group sessions at the university or individually by students who had volunteered to participate in the data collection. For the Belgian sample, exactly the same data collection procedure as in the French sample was used at the University of Liège among undergraduate volunteers.
Ethics Statement Participation in the study was voluntary and participants could quit the study at any time they wished. Participants were aware that information would be used in a scientific study. Identifying information about the participants was kept under lock and key at a different place than the dataset itself by the principal investigator in each country. For a self-report research with adults from a community-sample, the approval by Ethics Committee was not required at the time the study was conducted for the three countries involved. Institutional review boards exempt researchers from approaching the committee for such kind of research.
Analysis strategy The assumption of unidimensionality of the 25 facet and the five domain scales was tested with both parallel analyses and reliability analyses computed with SPSS 22. 0. Parallel analyses based on 1,000 random permutations of the original data were used. They make it possible to determine how many factors to extract for each facet and domain scale  [31]. They are based on a comparison between eigenvalues from a factor analysis of the actual data and eigenvalues from a factor analysis of a random dataset. The number of factors to be retained is based on the number of actual data eigenvalues higher than the upper 95% confidence limit of random data eigenvalues  [31]. To evaluate the internal consistency of the scales, we calculated Cronbach's alpha coefficients (α). Descriptive statistics were then produced for the 25 facets and the five domains.
Data collection and analysis We applied standard Cochrane methods to select eligible studies for inclusion and to collect data and assess risk of bias. We calculated study-level e ect sizes as standardised mean di erences (SMDs) between comparison groups, measured as quantities selected or consumed. We combined these results using random-e ects meta-analysis models to estimate summary e ect sizes (SMDs with 95% confidence intervals (CIs)) for each outcome for size and shape comparisons. We rated the overall quality of evidence using the GRADE system. Finally, we used meta-regression analysis to investigate statistical associations between summary e ect sizes and variant study, intervention or participant characteristics. Portion, package or tableware size for changing selection and consumption of food, alcohol and tobacco (Review).
Search methods We searched CENTRAL, MEDLINE, EMBASE, PsycINFO, eight other published or grey literature databases, trial registries and key websites up to November 2012, followed by citation searches and contacts with study authors. This original search identified eligible studies published up to July 2013, which are fully incorporated into the review. We conducted an updated search up to 30 January 2015 but further eligible studies are not yet fully incorporated due to their minimal potential to change the conclusions.
Library Trusted evidence. Informed decisions. Better health. Cochrane Database of Systematic Reviews recipients' levels of literacy, numeracy and cognitive control, which have been found to be lower in population subgroups experiencing higher levels of social and material deprivation  (Kutner 2006; Marteau 2012; Spears 2010; Williams 2003). Despite evidence that behaviours with the potential to undermine health are socially patterned (for example, that people in lower socioeconomic groups tend to consume less fruit and vegetables (Giskes 2010)), potential di erences in behavioural responses to product sizing interventions between socioeconomic subgroups remain unclear. Also, to our knowledge (prior to conducting this review), no studies of the e ects of product size had been conducted in low or middleincome (LMIC) country populations (Hollands 2013a).
Library Trusted evidence. Informed decisions. Better health. Cochrane Database of Systematic Reviews consumption of the manipulated product cannot be considered unregulated (ad libitum). • Studies that compared packages, portions, individual units or tableware of di erent types or with di erent functions. For example, we excluded studies that made comparisons between di erent, di erently sized eating utensils (e. g. straw versus spoon; chopsticks versus fork) whilst studies that made comparisons between di erent sizes of the same eating utensil were included (e. g. small spoon versus large spoon). • Studies in which there were concurrent interventions unrelated to sizing that were intrinsically confounded with the comparison(s) of interest. For example, we excluded two-arm studies in which one comparison group received a specified portion size and the other group received a smaller portion plus a concurrent nutritional labelling intervention.
Library Trusted evidence. Informed decisions. Better health. Cochrane Database of Systematic Reviews Within the conceptual model (Figure  1 ) we distinguished between three sets of potential e ect modifiers: study characteristics; intervention characteristics; and participant characteristics. Within our analytic framework for quantitative synthesis of outcome data collected from the included studies (see Data collection and analysis), potential e ect-modifying impacts of participant characteristics could in practice only be investigated based on between-study comparisons, due to lack of reporting of results by participant subgroups within the included studies.
Electronic searches We conducted electronic searches for eligible studies within each of the following databases: • Cochrane Database of Systematic Reviews • Social Sciences Citation Index (Web of Knowledge) (1956 to 30 January 2015); • Trials Register of Promoting Health Interventions (EPPI Centre)  (2004 ( to 30 January 2015)). We developed a MEDLINE search strategy by combining sets of controlled vocabulary and free-text search terms based on the eligibility criteria described above (see Criteria for considering studies for this review). This was externally peer-reviewed by an information retrieval specialist and Co-convenor of the Cochrane Information Retrieval Methods Group and revised based on their peer-review comments. We tested the MEDLINE search strategy for its sensitivity to retrieve a reference set of 48 records of reports of potentially eligible studies known to be indexed in MEDLINE that were identified by our preceding scoping review (Hollands 2013a).
Searching other resources We conducted electronic searches of two grey literature resources using search strategies adapted from the final MEDLINE search strategy: • Conference Proceedings Citation Index -Science (Web of Knowledge) (1990 to 30 January 2015); • Conference Proceedings Citation Index -Social Science & Humanities (Web of Knowledge) (1990 to 30 January 2015); • Open  Grey -www. opengrey. eu (1980 to 30 January 2015). We also searched trial registers (ClinicalTrials. gov and the World Health Organization International Clinical Trials Registry Platform (ICTRP)) to identify registered trials, and the websites of the following key organisations in the area of health and nutrition: • Centers for Disease Control and Prevention, USA; • EU Platform for Action on Diet, Physical Activity and Health; • International Obesity Task Force; • Rudd Centre for Food Policy and Obesity, USA; • UK Department of Health; • World Health Organization.