dataesr/scientific-paragraphs-categorization huggingface dataset:
Machines 2018, 6, x FOR PEER REVIEW 5 of 15 Hence, the above presented material parameters were not constant within the fatigue analysis, but depended on the local hardness condition. As observed within the fatigue tests using the IH and IH+StrP specimens, the crack initiation origin was located either right at the very surface, or within the bulk material at the transition from the hardened surface layer to the core region. For simplification, the hardness values were determined for these two points, whereat two material parameter sets were utilized in the course of the fatigue assessment. As low-cycle fatigue test results for the IH hardened material layer were available, wherein the values were in line with the ones based on the estimation from UML, this material data set was used. As no low-cycle fatigue tests were performed for the core region, the estimation based on the local Vicker's hardness was applied.
• Open sourcing can also be a relevant criterion since it fosters model and code sharing inside the community. From our current knowledge, only Oemof-Solph and the open-source tool OMEGAlpes (Optimization ModEls Generation As Linear Programming for Energy Systems) we are developing in our team seem to comply with such requirements. OMEGAlpes is dedicated to the generation of linear optimization problems for energy systems  [32]. It allows quickly building Mixed-Integer Linear Programs (MILP) to design and manage multi-carrier energy systems. OMEGAlpes models are based on energy flows and energy units allowing to quickly study numerous cases by setting and gathering elementary models. Big optimization problems (hundreds of decision variables) can be quickly solved at the district scale due to linear models.
• Data heterogeneity, due to the many various forms such data can hold. Since data accessibility is more relevant to organizational problems and policies, the scientific challenge in exploiting these data is more related to their variability and heterogeneity. Indeed, four main axes of heterogeneity are observed: quantity, granularity, structure and semantics (see Figure  3 ). Therefore, to use these data for modeling purposes, one needs to apply appropriate tools and techniques to handle this heterogeneity. The problem of data management is commonly encountered in the IT industry. In numerous application fields (online sales, social networks, advertising, etc. ), developers have to handle various and sometimes unreliable data, encoded in different formats and databases. Generally, data are stored in different and specific databases (data warehouses) and not exploitable as is.
One must then develop a middleware layer to extract the data, transform them and load them to client databases and interfaces to comply with clients' needs. Our problem here is quite similar: we want to extract district-related data from various origins and encoded on various files, pre-treat these data and store them in models able to perform simulation and/or optimization tasks. Consequently, this approach of Extraction-Transformation-Loading (ETL) seems well adapted to the problem of district modeling from existing data. The reader can find general information about ETL techniques in  [37].
• Individual parameters are often missing and then generated using statistical databases. Among UBEM tools, TEASER is particularly adapted to the generation of low order models with few input data. This Python package developed at the University of Aachen can generate a simple "archetype" model of a building with a minimum of five parameters and can involve statistical databases for data enrichment. The generated models are Python objects translated in Modelica using IBPSA annex 60 or Aixlib libraries  [40]. We build here "four walls elements (i. e. , interior walls, exterior walls, floor plate, and roof) SingleFamilyDwelling" archetypes models using TEASER according to the IWU (Institut Wohnen und Umwelt-Institute for Housing and Environment) topology issued from the EPISCOPE project  [36].
To complete missing data for OMEGAlpes models, one can extract parameters generated by TEASER in archetypes. For each parameter parsing, injection or extraction, one has to deal with different formulations and units. The generated dataset used to build all OMEGAlpes building models is summarized in Table  1. To apply this workflow, we developed a specific Python package to ease file parsing, data manipulation (with an intermediate SQLite database) and district model generation, with modularity in mind (for further data integration). The architecture of this tool is summarized in Figure  7. Such an approach is close to the ETL methodology. As ETL processes are well suitable for UML modeling  [44] , the choice of an Object-Oriented Programming language such as Python is appropriate. Besides, the support of Python by the scientific community eases the development of the "Transform" part.
Optimization The study cases presented in this paper are realized for a time step of 10 min for two weeks in January. For the first one, each optimization problem generated is composed of 38k variables (28k continuous and 10k binaries) for 61k constraints. The resolution was launched on an Intel bicore i5 2. 4 GHz CPU with the Gurobi solver so that the optimization problem was solved within less than 10 s on average for 192 optimizations. The corresponding results are detailed Section 4. 1. 2. The second study case consists of a single resolution since only one configuration is studied. The associated optimization problem is composed of 1211k variables (1100k continuous and 111k binaries) for 1263k constraints. The resolution was launched on the same Intel bicore i5 2. 4 GHz CPU with the Gurobi solver and the optimization problem was solved within 23 min.
Microscopy Cells were pregrown and inoculated in 1 ml complete synthetic medium in 24-well plates overnight so that they reached an OD of 0. 3-0. 8 for the experiment. Cells were then transferred to 384-well plates (Matrical) or 8-well Ibidi dishes coated with concanavalin A (stock solution of 0. 2 mg/ml, air-dried). All imaging of live cells was performed at 25°C. Images for Fig. 5  A were acquired on a Spinning Disk microscope (Yokogawa Confocal Scanner Unit CSU-W1-T2) built on a Nikon TiE body and controlled with the VisiVIEW software and a 100× NA 1. 49 CFI Apo total internal reflection fluorescence objective. The camera was an iXon Ultra EMCCD (Andor), and excitation lasers were a diode pumped solid-state (DPSS) 488 nm (200 mW) and a diode 561 nm (200 mW) laser. Z scanning was performed in streaming mode with a LUDL BioPrecision2 Piezo Stage with 100-ms exposure times per frame.
NuRIM fluorescence intensity measurements NuRIM fluorescence intensity quantification experiments were performed on a temperature-controlled Nikon Ti Eclipse equipped with a Spectra X LED lamp using a Apochromat VC 100× objective NA 1. 4 (Nikon; filters: spectra emission filters 475/28 and 542/27 and DAPI/FITC/Cy3/Cy5 Quad HC Filterset with 410/504/582/669 HC Quad dichroic and a 440/521/607/ 700 HC quadband filter  [Semrock] ) with exposure times of 500 ms in the GFP and 1 s in the RFP channel. 30 image frames were imaged per sample and condition. Images were processed using NuRIM code  (Rajoo et al. , 2018; Vallotton et al. , 2019)  in MATLAB (MathWorks). Briefly, this method uses the HDEL-DsRed signal to generate a mask that segments the nuclear envelope and then extracts intensity from this mask.
Imaging was conducted with 1. 5% laser intensity with a gain of 800 to illuminate the GFP, and 0. 3% of the 561 laser power to illuminate the HDEL with DsRed was used during prebleach and postbleach acquisition. The photobleaching was performed by manually defining an elliptical region comprising approximately one third of the cell nucleus. The mobility of GFPlabeled proteins in the bleached nuclear envelope region was evaluated by quantifying the signal recovery in the bleached region. Extracellular background (I bg ) was subtracted from the intensity of the bleached region (I bl ), and the values were bleach-corrected by normalizing for total cell intensity (I total ) resulting in (I bl -I bg )/(I total -I bg )  (Bancaud et al. , 2010). The datasets acquired during three independent experiments were processed using MATLAB with custom-written scripts.
FCS FCS experiments were performed with a Leica TCS SP8 STED/ FLIM/FCS microscope equipped with a 63× 1. 2 NA W HC PL APO CS2 objective with the HyD 2 SMD (Single Molecule Detection; 500-550 nm) and HyD 4 SMD (604-750 nm). The Leica components were used in combination with the PicoHarp 300, TCSPC Module (Time Correlated Single Photon Counting system), 4 Channel Detector Router (PHR 800), and the Software SymPho Time 64 1. 6. FCS experiments were conducted using the 488-nm argon laser line at 20% power set to 0. 1-0. 2% for acquisition and the pulsed white light laser at 70% with a laser pulse of 20 MHz at 594 nm set to 0. 3-0. 7% for acquisition. Cells were imaged for 30 s, and traces were recorded and saved by the PicoQuant software. FCS measurements were performed in diploid yeast cells in the center of the nucleus in order to place the entire confocal volume with high confidence within the nuclear boundary.
After four washing steps (10% formamide/2× SSC; 0. 1% Triton/2× SSC; 2× SSC/DAPI; and 2× SSC), cells were stored at 4°C. Cells were imaged in concanavalin A-coated 384 wells. Microscopy was performed with an inverted epifluorescence microscope (Nikon Ti) equipped with a Spectra X LED light source and a Hamamatsu Flash 4. 0 scientific complementary metal-oxide-semiconductor camera using a PlanApo 100× NA 1. 4 oil-immersion objective (Nikon) and the NIS Elements software. 27 z slices were acquired at 200-nm spacing. Online supplemental material Fig. S1  shows that RNA Pol II and RNA Pol I can be efficiently degraded via an AID (control experiments related to Fig. 3 ). Fig. S2  shows that Dbp5 can be efficiently degraded via an AID and that depletion of Gle1 leads to loss of Dbp5 from the nuclear envelope (control experiments related to Fig.
Acknowledgments We thank  Malthe Wachsmuth  for his help with Fluctuation Analyzer 4G for FCS data analyses. We are grateful to  Justine Kusch  from  ScopeM, ETH Zürich , for microscopy support, and to  Catherine Dargemont  for providing the anti-Mex67 antibody. We thank members of the  Weis group  for discussions and critical reading of the manuscript. We are particularly grateful to  Stephanie Heinrich  for helpful discussions and advice. K. Weis  acknowledges support from the  Swiss National Science Foundation  (project number  31003A_179275 ). The authors declare no competing financial interests.
Using the motorised micromanipulator, the monolayers could be extended to different strains with controlled strain rates. Extended monolayers exerted restoring forces on the flexible rod, causing the transducer tip to bend. The extent of bending was translated into a voltage value that was converted into a digital signal using a data acquisition system (USB-1608G, Measurement Computing) and recorded onto a computer. Both the data acquisition system and the motorised micromanipulator were controlled with a custom-written code in Labview. The monolayer and the transducer tip were imaged every 0. 5 s using a 2× objective (2× PLN, Olympus). The mechanical testing procedure consisted of several steps: -Initial approach: The tip of the force transducer was initially brought into contact with the Tygon tubing and then positioned such that the left tweezer arm was out of contact but within 50 μm distance from the Tygon tubing.
To estimate τ, τ(5 < t < 20 s) -B was plotted in a semi-logarithmic scale and fitted with a line, with μ being the slope of this line (Fig  S3c ). Each experimental relaxation curve was fitted using equation (  1 ), with the free parameters A, α, and τ. B was also allowed to vary by 15% to optimise the fits (Fig  S3d ). The trustregion-reflective least squares algorithm, a built-in Matlab fitting procedure, was used for the fitting. The fitting was performed for the three individual repeats of the stress relaxation experiments on each monolayer. The fitted values obtained from the three repeats were then averaged to obtain a single value for each parameter. For ATP depletion experiments, we followed the same general procedure except that we fitted experimental curves with a function of the form At -α.
Statistical analysis All data analysis and curve fitting were conducted using custom-written code in Matlab. For each dataset, outliers were defined as the values that fell outside the range [q 1 -w × (q 3 -q 1 ),q 3 + w × (q 3 -q 1 )], where q 1 and q 3 were the 25th and 75th percentiles of the data and w was 1. 5. Outliers were excluded from statistical analysis. The normality of the data was tested using both Lilliefors and Shapiro-Wilk tests in R, which confirmed non-normality of some datasets. Statistical analysis was performed in Matlab, using a two-sided Wilcoxon rank sum test that does not assume normality of the data. To determine whether a single dataset was significantly different from zero, Wilcoxon signed rank test in Matlab was used. Datasets with p < 0. 01 were deemed to be highly significantly different and are denoted by a double asterisk (**).
Datasets with p < 0. 05 were deemed to be significantly different and are denoted by a single asterisk (*). Changes with p > 0. 05 or where statistical power was less than 0. 8 were considered non-significant. For all boxplots, the edges of the box represent the 25th and 75th percentiles of the data, the red line marks the median and the whiskers extend to include the most extreme data points that are not considered to be outliers. Points on each boxplot represent individual monolayers or cells. Each dataset is pooled across experiments performed on at least 3 individual days.
Acknowledgements The authors wish to acknowledge present and past members of the  Charras, Baum, Kabla , and  Muñoz  labs for stimulating discussions. The authors acknowledge technical support from  UCL Genomics  for sequencing and analyzing total RNA data as well as from  Julia Duque  ( LCN ) for analysis of fluorescence intensity at junctions. N. K. was funded by the  Rosetrees Trust , the  UCL Graduate School , the  EPSRC  funded doctoral training program CoMPLEX, and the  European Research Council (ERC-CoG MolCellTissMech , agreement  647186  to GC). N. K. was in receipt of a  UCL Overseas Research Scholarship. N. K. was supported by the  Prof Rob Seymour Travel Bursary Fund  for research visits to Barcelona. J. F. and A. B. were funded by  BBSRC  grant ( BB/M003280  and  BB/ M002578 ) to G.
C. and A. K. J. J. M, N. A. and P. M. acknowledge the support of the  Ministry of Economy, Industry and Competitiveness (MINECO)  through Grants No. DPI2013-43727R , and  DPI2016-74929-R  and the  Generalitat de Catalunya  through Grant No. 2014-SGR-1471. N. A. was also financially supported by  Universitat Politècnica de Catalunya (UPC)  and  Consorci Escola Industrial de Barcelona (CEIB)  through Grant  UPC-FPI 2012 , and the  European Research Council  under the European Community's  7th Framework Programme  ( FP7/2007-2013)/ERC Grant  Agreement No. 240487. P. M. was also supported by the  European Molecular and Biology Organisation (EMBO)  under grant  ASTF 351-2016. RB is part of the  EPSRC  funded doctoral training program CoMPLEX. MD was funded by a  Marie Skłodowska-Curie Horizon 2020 Individual Fellowship  (MRTGS).
A. Y. was supported by an  HFSP   Young Investigator award  to  G. C. ( RGY 66/2013 ). A. H. was supported by  BBSRC  grant ( BB/K013521 ) to G. C. and A. K. Y. F. was supported by  Japan Society for the Promotion of Science (JSPS)   Grant-in-Aid for Scientific Research on Innovative Areas 26114001, Grant-in-Aid for Scientific Research  (A)  18H03994 ,  Strategic Japanese-Swiss Science and Technology Program ,  AMED  under Grant Number  JP17ck0106361  and  JP18cm0106234 ,  SAN-ESU GIKEN CO. LTD , the  Naito Foundation , and the  Takeda Science Foundation. A. K. was supported by  BBSRC  grants ( BB/K018175/1 ,  BB/M003280  and  BB/M002578 ). Y. M. is funded by a  MRC  Fellowship  MR/L009056/1 , a  UCL Excellence Fellowship , and a  NSFC   International Young Scientist Fellowship 31650110472.
Although MDA is known to introduce biases in the relative abundance of sequences in complex samples  42  , this provided valuable information in low biomass environments such as sediments or permafrost  43, 44. Shotgun libraries were generated using Nextera XT DNA Sample Preparation Kit. These were controlled for quality on Agilent High Sensitivity microarray, and mixed in an equimolar pool for sequencing (2 × 300 bp paired-end Illumina MiSeq; Genoscreen, Lille, France). Experimental blank controls led to exploitable library only for MT. The corresponding sequence files were deposited to NCBI's Sequence Read Archive (BioProject ID PRJEB25763) with the sample accession numbers ERS2351639 to ERS2351645. Sequence processing and data analysis. All sequences were processed using free software and custom Perl scripts, using the regional calcul center Mesocentre Clermont Auvergne (in general 32 CPUs with 128 Go of RAM were used here), and locally under Linux Ubuntu operated computers.
Sequences were first quality controlled (FastQC version 0. 11. 3; Babraham Bioinformatics) and trimmed for removing low quality ends, sequences < 40 bp or containing ambiguous bases using PRINSEQ-LITE  45. Mate pairs were assembled using PANDAseq Assembler version 2. 8  46  ; final sequence length was ~300 bp. Potential contaminant sequences were removed by alignment against the experimental control using BWA-MEM  47  and SAMtools  48. Taxonomic annotations of prokaryotes and eukaryotes were obtained using BLASTN  49  against the ribosomal database SILVA 119. 1 SSURef Nr99 50 ; the best hits with an alignment e-value < 0. 01 were recovered. Chloroplasts and mitochondria sequences were manually removed from the analysis. Functional annotations were performed using BLASTX  49  against the protein database UniprotKB  51  , restricted to cellular organisms (Archaea, Bacteria and Eukaryotes) and viruses.
The best hits with an alignment e-value <10 -4 were collected. Data were then analyzed using gene ontologies (GO IDs terms defined by the Gene Ontology Consortium)  [52] [53] [54]  associated with UniprotKB identifiers and using notably the following informatics tools and databases: Protein Information Resource PIR  55  , REVIGO  56  , GOSlimViewer 57 , AmiGO  53  , and KEGG  [58] [59] [60]. In total, 2670 to 3373 unique GO terms were found in each cloud water MG or MT dataset. For comparative analysis, other sequence files were collected from literature studies selected for including both metagenomes and metatranscriptomes (Table  S1 ), and these were reprocessed similarly as our data using our bioinformatic pipeline. When necessary, data were rarefied to ~400,000 sequences to remain consistent between datasets.
Rarefaction may have decreased the sensitivity of the intercomparison, but this is acceptable in the frame of our descriptive study looking for large differences originating from multiple features (GO terms), rather than a direct comparison of the abundance of specific sequences  61. GO terms represented by ≥0. 05% of the sequences in at least one MT dataset were considered (823 GO terms in total; Table  S2 ). Among those, 724 occured in cloud MT and 764 in MG, and 699 were present in both MT and the corresponding MG: 317 of these related to a Molecular Function (MF), 313 to a Biological Process (BP), and 69 to a Cellular Component (CC). Their corresponding rank-abundance in MT and relative abundance respect to MG are shown in Figs  S1  and S2. The GO were finally manually classified according to functional categories, and their relative level of expression was assessed through their relative contribution in MT (i.
Functional analysis. In order to examine community functioning, we explored the gene ontologies (GO) associated with identified protein sequences. The relative distribution of GO terms among MG and MT datasets was variable, thus highlighting metabolic requirements within community's capabilities. Overall, metabolic processes oriented toward energy production (catabolism) and transports dominated (Fig. 2 ). Accordingly, among cellular components, membranes and ribosomes were highly represented in MT, relatively to MG. Meanwhile, cellular nucleus and mitochondria tended to be underrepresented in MT compared with MG, supporting prokaryotes as the active microorganisms. In the absence of reference, the information provided per se by metranscriptomes for examining the microbial life conditions in a particular environment is limited.
Such variations on such a short period of time illustrates the great short-term variability of biodiversity existing in the atmosphere  72. Moreover, large short-term variations of the expression of biological functions are regularly reported in other environments like seawater  73. The main results of the functional analysis are presented in Fig. 4  and the corresponding data are shown in Table  S2 ; these are to be consulted throughout this section. More details are presented in SI Figures, which are referenced when appropriate. These unique datasets allowed us to draw an unprecedented picture of cloud microbiota's functioning and of its relationships with its temporary cloud water habitat. Our analysis revealed the expression of biological functions reflecting a challenging environment, largely oriented toward the response to a demanding environment as one can figure: oxidant, cold, and subjected to osmotic variations.
See Table  S2  for the complete GO term list and the number of corresponding sequences in each dataset. systems in bacteria; GO:0000155 and GO:0000160 79 ) (Fig. S4 ). The high calcium transport activity observed was potentially acting in signal transduction  80. Despite temperatures close to freezing during sampling, translational activity, i. e. protein synthesis was maintained (Fig. S7 ). This is consistent with the reported capacity of cloud microbial communities to uptake  14  C labelled leucine at 0 °C25. In all likelihood ammonium, abundant in the samples (Table  S1 ), was taken up from clouds water and fed amino-acid metabolism with nitrogen (Fig. S5 )  81. This was oriented toward valine, glycine, glutamate and glutamine metabolisms; in addition of permitting new proteins synthesis implied by metabolic arrangements, glycine and glutamate are constituents of gluthathione, the main intracellular redox regulator.
Acknowledgements The authors thank in particular  L. Nauton  for help with informatics,  J. -M. Pichon  for meteorological data, F. Enault and  B. Ervens  for constructive comments on manuscript, and  G. Lefebvre  for language correction. Two anonymous reviewers are also acknowledged for their constructive remarks. This project was funded by  CNRS   EC2CO  ( MicrobiEN) program , " FONCOMIC " project attributed to PA. MJ and LB were hired on the projects  ANR-15-CE01-0002  (" INHALE" ) and  ANR-DFG-14-CE35-005-02  ("CHLOROFILTER"), respectively.
Statistical analysis The current study was an exploratory study, thus, there was no statistical hypothesis or a minimum number of patients to include. Comparison of clinical characteristics between the patient groups was performed using the Mann-Whitney test or Fisher's exact test depending on the distribution of the variables. Categorical comparisons were performed using the Chi2 test. Evaluation of PFS and of OS was performed using the log-rank test (Kaplan-Meier method). Statistical analysis was performed using XL STAT 2018 (Addinsoft). A p-value inferior to 0. 05 was considered as significant. Anonymized study database can be found as S1 File.