dataesr/scientific-paragraphs-categorization huggingface dataset:
The spectrometer was operated in a data-dependent mode, automatically switching to MS/MS mode. MS-spectra were acquired in the FTICR, while MS/MS-spectra were acquired in the LTQ-trap. For each scan of FTICR, the three most intense, doubly or triply charged, ions were sequentially fragmented in the linear trap by collision induced dissociation. All the tandem mass spectra were searched by MASCOT (Matrix Science, London, UK) program to identify proteins. The analysis was repeated two times. Samples without RNA were processed in parallel and were used as negative controls to identify proteins that bound non-specifically to the beads. The experiment was performed twice and proteins identified in both biological replicates, but not in the negative controls, were taken forward.
Computational analysis of identified proteins Bio-computational analysis of the proteins identified by LC-MS/MS was carried out based on their biological functions and cellular localization by using the DAVID functional annotation tool (http://www. david. abcc. ncifcrf. gov) for Gene Ontology (GO) analysis relative to RNArelated processes and cellular localization, and by literature search. To identify conserved RNA-binding domains, the specialized blast from NCBI was used, searching against Conserved Domain Database (CCD) database (http://www. ncbi. nlm. nih. gov/Structure/cdd/cdd. shtml), which includes NCBI curated 3D protein domains, as well as data from 5 among the major sources of protein families databases  [52]. This analysis was further improved by searching the proteins that were identified in RBPDB database (http://rbpdb.
We screened the candidate proteins for the presence of known functional RNA-binding domains (e. g. , RRM, KH, RNA helicase domain, WD40, and YTH) using the NCBI tool CDS. In addition, we performed a gene ontology (GO) term molecular function analysis of all identified proteins using the DAVID annotation tool (https://david. ncifcrf. gov/), considering the GO terms related to RNA binding, processing, splicing, mRNA/ncRNA metabolic processing, and the RNP complex. The protein classification was improved further by a literature search to identify experimental evidence of the involvement of the proteins in direct or indirect RNA binding. To understand the direct and indirect functional associations between the query protein sets, we used Ingenuity Pathway Analysis (IPA) software to build a biological network of the RBPs in exosomes and their parental cells.
thermofisher. com/order/genome-database/browse/gene-expression/keyword/ hs00245438_m1?ICID=uc-gex-hs00245438_m1&mode=and 9961 MVP-rev https://doi. org/10. 1371/journal. pone. 0195969. t001 DDct ¼ Dct ðMVPÞ À Dct ðneg:ctl:Þ RQ ¼ 2 DDct.
Selected resolutions include those normally implemented in existing metering systems, e. g. , 900 s is the resolution of smart meters in Switzerland and Italy. Here, the intent is to verify whether such a sampling time is enough for the proposed application, or if performance would benefit from more densely sampled data. Each of the 7 datasets at different resolution is further split into 3 sub-sequences to perform a three-fold cross-validation, i. e. , for each resolution, the algorithms are trained on the first fold and tested on the remaining 2; the process is repeated for all the 7 datasets, each time testing the algorithms on the part of the data which is not used for the training. In total, each algorithm is trained and tested 3 times for each resolution, for a total of 21 training and test runs.
a) PV and power flow measurements: The power flow at the PCC is the sum of the four households flows, measured synchronously at 10 s resolution. Similarly, the global PV production (used as the ground truth value to validate the estimation performance of the algorithms) is the sum of the single PV facilities power injections, measured at the converter level. b) Global horizontal irradiance measurements: GHI measurements are from a pyranometer installed on the roof of the household ID1. The line distance of the remaining households from the GHI observation point is shown in the second to last column of Table  I. All the measurements are synchronized and timestamped, and logged in a time series database.
VI. CONCLUSIONS The problem of disaggregating a sequence of active power flow measurements composed of unobserved PV generation and demand into the respective trajectories was considered. Four disaggregation algorithms were discussed. They attempt to explain similarities between the time series of the aggregated and estimated PV generation, three in the time domain and one in the frequency domain. Estimation algorithms 4 Computational times refer to a workstation equipped with an Intel Xeon processor running at 2. 70 GHz with Matlab on a virtualized operating system. Method C and Method A,B and D were executed on two different machines, machine 1 and 2. The computation time of Method C was adjusted by a factor t 2 /t 1 , where t 1 and t 2 is the computation time of a reference problem executed on machine 1 and 2, respectively.
Algorithm performance was tested with data from a real-life setup, with PV generation from multiple sites with different configurations, different demand profiles, and battery systems for PV self-consumption. Results show that the best performing algorithms estimate PV generation with a root mean square and mean estimation errors in the ranges 3. 4 ÷ 8. 8% and 0. 5 ÷ 2. 3%, respectively, and that performance is minimally affected by the level of PV penetration in the prosumption mix. The practical utility of the proposed algorithms is envisaged in the context of power and energy management of distributed energy resources and data-driven PV generation forecasting in those situations where information from PV plants is not available due to issues such as privacy concerns or lack of adequate communication infrastructures.
Primers and probe design and qPCR run protocol Primers (O. l. F 5 0 -GGAGGTGGTCCTGGTAGTAG-3 0 ; O. l. R 5 0 -GCAAACCCAAAACTATA GTATCC-3 0 ) and a TaqMan-MGB hydrolysis probe (FAM-5'-CTTAGAGTAGAGGGTCAG CC-3'-non-fluorescent quencher-MGB; Applied Biosystems; Foster City, CA, USA), targeting partial cox1 gene (90bp), were designed by alignment of sequences from a wide range of closely related filarial nematodes available from GenBank database (Table  2 ), using Primer Express 2. 0 (Applied Biosystems, Foster City, CA). Specificity of the primers and probe for O. lupi were confirmed in silico using the basic local alignment search tool (BLAST, GenBank, NCBI). qPCR reactions were carried out in a final volume of 20μl, consisting of 10μl of IQ Supermix (Bio-Rad Laboratories, Hercules CA, USA), 7.
1μl of Di-Ethyl Pyro-Carbonate (DEPC) treated pyrogen-free DNase/RNase-free water (Invitrogen, Carlsbad, CA, USA), 2μl of template DNA (except no-template controls), 5 pmol and 0. 5 pmol for primers and probe, respectively. The run protocol consisted of a hot-start at 95˚C for 3 min, and 40 cycles of denaturation (95˚C for 10 sec) and annealing-extension (64˚C for 30 sec). All assays were carried out in duplicate and a no-template control was included in each run. The qPCR was performed in a CFX96 Real-Time System (Bio-Rad Laboratories, Inc. , Hercules CA, USA) and the increase in the fluorescent signal was registered during the extension step of the reaction and analysed by the CFX Manager Software Version 3. 1 (Bio-Rad).
Specificity and sensitivity of cPCR The analytical specificity and sensitivity of the cPCR for the specific amplification of cox1 gene fragment (*689bp;  [32] ) was assessed by testing genomic DNA of: i) skin samples with different parasitic load of O. lupi (Table  3 ), ii) serial dilution of O. lupi mfs DNA (i. e. , from 3. 6 ×10 1 pg/2μl to 3. 6 ×10 -3 pg/2μl) and iii) DNA of adult specimens (i. e. , from 8 ×10 1 ng/2μl to 8 x 10 -3 fg/2μl). All cPCR products were resolved in 0. 5x GelRed stained (Biotium, CA, USA) agarose gels (2%), purified using enzymatic purification (Exo I-FastAP; Thermo Fisher Scientific, MA, USA) and sequenced in an automated sequencer (3130 Genetic Analyzer). All sequences generated were compared with those available in GenBank using Basic Local Alignment Search Tool (BLAST)  [33].
Out of 152 blackflies, mosquitoes and midges, eight Simulim spp. (n = 1 S. erythrocephalum; n = 1 S. ornatum; n = 6 Simulium sp. ), experimentally infected and died from 1 to three days post infection, returned positive signal for O. lupi DNA (Table  4 ). All field-collected blackflies and mosquitoes were negative for O. lupi DNA using qPCR (Table  4 ). All blackflies positive for qPCR scored positive also for cPCR. Sequences derived from all amplicons of cPCR matched with 99-100% nucleotide identity appropriate reference sequences of O. lupi available from GenBank (accession numbers KC686702, KC686701).
This is clearly observed in the AFM analysis whose results were discussed just below (Fig. 4 ), showing that the roughness present also two behaviours delimited by the 3% Sr-doping. Surface morphology was characterized with atomic force microscope (AFM, Model Dimension Edge of Bruker) operating at room temperature in a tapping mode, and the images were treated using WSxM software  [27]. We have used the scanning area 3 mm Â 3 mm for the study of surface morphology. AFM images were acquired with a resolution of 512 Â 512 pixels. The AFM analysis allows us to determine the surface roughness. The roughness is defined either by the mean square roughness s (rms) or the average Fig. 1. Simplified scheme of the ultrasonic pyrolysis technique. roughness s a. These two roughnesses are defined by the following expressions  [28] : sðrmsÞ ¼ P N i¼1 ðZ i À ZavgÞ 2 N !1 2 (2) and s a ¼ P N i¼1 ðZ i À ZavgÞ N ( 3 ) where N is the number of points, Z i is the number of ith point of Z and Zavg is the average value of Z.
These two expressions of the roughnesses were treated by WsXM software and their profiles were extracted from AFM images as shown in Fig. 3    The AFM characterization of the pristine ZnO film revealed homogeneous and continuous surface uniformly distributed nanometre sized grains where the surface roughness srms is determined to be ~4 nm. The state of the surface changed under Srdoping effect and the ZnO films became granular with irregular ZnO particles and somewhat porous. As shown in Fig. 4(c ), the 3% Sr-concentration affected noticeably the surface morphology where ZnO nanoparticles agglomerated on the surface and formed flower-like clusters. The clusters grew with increasing Sr concentration from 1 to 3% and became large-sized grains covering partially the surface and reducing thus the roughness for 3% Srdoping.
The increase and decrease of the roughness with Srdoping (Fig. 5 ) are in agreement with what we have observed on the grain size determined from the (002) XRD peak and especially on the shift of this peak at the 3% Sr-doping. Overall, the change on the surface morphology of ZnO was observed as a result of the Srdoping effect. In order to complete the study of the surface morphology, the particle size of pristine and doped ZnO thin films were evaluated by the WSxM software analysis. According to the calculated grain size and as reported in Table  1 , the cluster size is 40 nm for pristine ZnO, 150 nm for 1%, 180 nm for 2%, 200 nm for 3%, 172 nm for 4%, and 153 nm for 5% Sr-doping. We also observed that the 3% Sr-doping is the limit between two different behaviours of the ZnO films. In the doping range 0e3%, the cluster size increased whereas it decreased for 4 and 5% Sr-doping.
Sparseness  [1]  is another short text issue. Indeed, vectors representing short texts are very sparse. Short texts lack also of contextual information, which implies a poor coverage of classes representing the dataset. The classification of new data becomes then complicated compared to standard texts. Owing to these problems, bag-of-words representation combined with traditional machine learning algorithms is not efficient for short text classification. Works to overcome these issues try to propose alternative representations based on either enrichment or reduction of features. Reduction and enrichment use generally external sources of information to link features semantically. External knowledge can be taken from ontologies (wordNet, Wikipedia)  [12, 13]  or from large scale datasets on which topic models techniques are applied (LDA, Latent Semantic Indexing  [14].
The Enrichment Method To overcome the short text classification issues, as done in  [1] , we enrich short texts with words semantically related to their content. Thus the vector representing a short text becomes longer and contains more information. This additional information is found in external knowledge sources contained in documents that are closely related to the domain of our dataset and at the same time that are general enough to cover all the concerned domain. Figure  1  represents our method of enrichment, Figure  2  and Figure  3  illustrate through an example the mechanism of transforming the same dataset into a bag-of-word representation, without and with enrichment process respectively. In order to extract knowledge from those external sources, we apply LDA which is a generative probabilistic method that uses Dirichlet distribution to identify hidden topics of a dataset.
Then, each word is associated to one or many of the generated topics based on similarity calculation. Each word has a given weight representing its importance in a topic. The external sources used to add more information in our dataset are then transformed in a set of n topics containing k words (Figure  3-b ). We start our enrichment process by considering a dataset D = {d i ,. , d m } composed of m short texts (Figure  3-a ). The first phase of our text enrichment method is local: it is a word level enrichment. During this first step, we add to each word contained in each of the m short texts, all the words contained in the nearest topic that is the topic in which this word has the biggest weight (Figure  3-c ). Let us consider a document d i represented by a set of words d i = {w i1 ,. , w il }.
Finally, we generate a matrix corresponding to the new representation of our dataset from this new set of words. Each row of the matrix corresponds to a short text and each column corresponds to a word used after the two phases of enrichment. The value contained in a cell corresponding to the short text i and the word j represents how many times this word appears in the enriched short text. Comparing this last representation to the initial representation (Figure  2 -f) we can see that vectors are larger and contain less null values. Thanks to this transformation, short text classification will be improved as confirmed by the next section.
Semantic Random Forest In order to compare our results with the ones obtained by Bouaziz in  [6]  by using a semantic Random Forest method, in this section, we first present such a method before presenting the semantic method we propose in this paper. In RF algorithms, short text dataset is represented by a matrix where columns are all the words composing the dataset after preprocessing -the features. Each line of the matrix is an enriched short text. The values in the line represent the importance of the features in the text. There are several ways to compute the importance of a word in a text: number of occurrences, term frequency, tf-idf and so on. To build each decision tree of traditional RF, a subset of the matrix lines is randomly selected to form a bootstrap and the whole feature space is considered.
Search snippets Dataset To validate our new method we tested it on the "search-snippets" dataset. This dataset was collected by Phan  [1]  and is composed of: -Short texts corpus: built from top 20 to 30 responses given by Google search engine for different queries. Each response is composed of an URL, a title and a short description. Each short text is labeled by a class according to the submitted query. -Universal dataset: this dataset is composed of a set of documents collected from Wikipedia as a response to queries containing some specific keywords. The application of LDA on this document set generated 200 topics with 200 words each. We used those topics as an external source for short text enrichment. To evaluate our algorithm, we used the accuracy defined as the ratio of correctly classified short texts to the total number of short texts.
Results and Interpretations In this section we evaluate the added value of our two-level enrichment method. We present the results of the enrichment at text level and the improvement obtained thanks to word level enrichment. We ran our tests using RF classifier in a first step, then we confirm our results by running similar experiments considering MaxEnt, SVM and Naive Bayes. Enrichment at whole text level In a first step we applied the enrichment of short text at whole text level to evaluate its contribution. We run classification experiments with traditional RF and an enrichment of 4 topics. This test was repeated with 10, 20 up to 100 trees. Enrichment at word level In order to evaluate the contribution of the second level of our enrichment model we run the following experiments: we take our short text dataset and we add to each text the nearest topics.
Conclusion In this work, we proposed a new approach of short text classification based on semantics in the pre-processing step by a new enrichment method and in the learning step by making decision trees building dependent on semantic relations between features. We first considered an external source of knowledge to our dataset. We applied LDA to generate semantic topics. Then we used a two level enrichment process to enlarge short text. The first level takes a text word by word and adds to each one the most similar topic. Whereas the second level considers a text as a whole entity and enriches it by the nearest topics with respect to its general meaning. Our second contribution is a new Semantic Random Forest where the classic Random Feature Selection algorithm at the node level is replaced by a Semantic Feature Selection.
Our enrichment provides a significant improvement when applied on the "search snippets" dataset and by using different classifiers like RF, SVM, max-Ent and Naive Bayes. Combining with the Semantic Random Forest we obtained a significant improvement. The Semantic Random Forest at Node level allowed an even better classification. The overall classification improvement reached 34% compared to RF. Moreover our Semantic Random Forests are composed of small decision trees (an average of 1911 nodes per tree for SRF Node level versus 6272 for RF) which makes them faster. For the future, we think that our semantic approach can be further improved by combining it with weighted Random Forest which are suitable for unbalanced dataset classification like "search snippets".
All analyses were conducted using Statistica 10. 0 software (Statsoft ® , Tulsa, OK USA). 37 A 2-tailed α level of ,0. 05 was adopted. Qualitative data of patients' behavioral and verbal manifestation during the interaction were informally analyzed. Each recorded intervention was examined by a trained psychologist (MD) who was briefed on the thematic analysis method (Braun and Clarke  38  ), though the verbalizations were not transcribed. For that, verbal and nonverbal mani festations were noted, classified into emerging themes and categories of behaviors, then discussed and validated with investigators. For the purpose of the current publication, verbal interactions were translated from French to English and verified by two bilingual neuropsychologists (VCL and MD).
, 2016) , this method increases the uncertainty in the quantification of HOM concentrations. However, as mentioned, a more accurate knowledge of the exact HOM con- centrations would not influence the main findings of this study. The MT, trace gases, and meteorological parameters were continuously monitored at the different heights  (4. 2, 8. 4, 16. 8, 33. 6, 50. 4, 67. 2, 101 , and 125 m) on a 126 m mast ∼ 100 m away from the location of the CI-APi-TOFs. The data at 4. 2 and 33. 6 m were used in this study to represent the concentrations at near ground and tower levels, respectively. MT concentrations were measured every third hour using a proton transfer reaction mass spectrometer with a lower detection limit of 1 pptv (PTR-MS, Ionicon Analytik GmbH;  Taipale et al. , 2008). The O 3 concentration was measured with a UV light absorption analyzer that had a lower detection limit of 1 ppbv (TEI model 49C, Thermo Fisher Scientific, USA).
Data overview The Influence of Biosphere-Atmosphere Interactions on the Reactive Nitrogen budget (IBAIRN) campaign was conducted from 1 to 25 September 2016. After data quality checks, only the measurements collected after 5 September were used. Figure  2  shows the overall time series of the meteorological parameters measured at ground and tower levels, including the temperature, RH, global radiation, concentrations of trace gases, MT, and total HOMs  (Zha, 2018). The weather was generally sunny and clear during the campaign except for a few cloudy (10, 15, and 22-23 September) and drizzling (24 and 25 September) days. The mean air temperature and RH observed at ground level were 10. 8 ± 3. 3 • C and 87±13 % (1σ standard deviation) and at the tower level were 10. 5±3. 0 • C and 88±14 %, respectively.
6 × 10 6 to 1. 7 × 10 6 cm -3 , and the concentration of the CHO dimer group decreased from 1. 5 × 10 6 to ∼ 0. 1×10 6 cm -3. The concentrations of the CHON monomer and CHON dimer groups also experienced large declines (∼ 34 % and ∼ 50 %, respectively) in the latter half of the night. At 03:00, the CHON dimer concentration was already below the detection limit (1 × 10 4 cm -3 ). Therefore, the much lower ground HOM concentrations might not be totally explained by the change of HOM production but rather due to some other processes such as additional losses. A previous study by  Alekseychik et al. (2013)  at the SMEAR II station showed that nocturnal decoupled air layers were frequently (with a fraction of 18. 6 % based on a long-term dataset) observed under high-Ri conditions in the boreal forest.
Acknowledgements. This work was supported by the  IBAIRN  project, the  Academy of Finland Center of Excellence in Atmospheric Science, European commission Actris2  and  Actris PPP , the  European Research Council  (grant  638703-COALA ), transnational access from ENVRI plus, and the  SMEAR II technical team. Qiaozhi Zha thanks ATM-DP  ( Doctoral Program in Atmospheric Sciences ) graduate programs,  John Crowley  and the  Max Plank Institute  in association with the IBAIRN proposal, and the tofTools team for providing tools for mass spectrometry analysis. Gabriel Katul acknowledges the support from the  US National Science Foundation  ( NSF-EAR-1344703 ,  NSF-AGS-1644382 ), the  U. S. Department of Energy  ( DE-SC0011461 ), and the  University of Helsinki  for supporting a 3-month sabbatical leave at the  Division of Atmospheric Sciences.
Data availability. The time series of the measured trace gases, meteorological parameters, and estimated total HOM concentrations at the ground and tower levels will be available at the end of December 2018 (https://doi. org/10. 5281/zenodo. 1489079, Zha, 2018). For other data please contact the first author via email (qiaozhi. zha@helsinki. fi). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-18-17437-2018-supplement.