dataesr/scientific-paragraphs-categorization huggingface dataset:
1 regulation of NeoR linked to the ESAG9 3'UTR was not lost upon deletion of this domain, demonstrating that regulation after REG9. 1 depletion does not operate through this sequence region alone (S2 Fig). Similarly, a MEME search (http://meme-suite. org) of transcripts co-regulated with ESAG9 mRNAs in our global transcriptome analysis identified short motifs enriched in the regulated transcripts distinct from the previously mapped regulatory element (AUGCGAAUAA and GAAAACAUGC/ GCGUACAUGA), but deletion of these motifs in the ESAG9 3'UTR did not consistently increase transcript expression when tested using a NeoR reporter system (S6 Fig). Hence, the 3'UTR sequences that determine the regulation of ESAG9 when REG9. 1 is depleted are expected to be multifactorial and dispersed. This is consistent with the analysis of 3'UTR regulatory signals across several studies where linear motifs have not been detected.
For the phenotypic analysis of REG9. 1 in procyclic forms, AnTat1. 1 90:13-REG9. 1 RNAi cell line was differentiated to procyclic forms and grown in SDM79 + 50mM N-Acetylglucosamine (GlcNAc) at 27˚C in 5% CO 2  [44]. Procyclic 29. 13 cell line was used as a control, grown in the same conditions. The neo-ESAG9 3'UTR reporter cell line and its variations were generated using the p5'neo5' vector, that contains the neo gene flanked by the aldolase 5' and 3'UTRs (derived from pbRn,  [45] , but with BLE resistance and the rRNA promoter removed). The different bloodstream form RNAi cell lines were generated using the stem loop vectors pRPa (for monomorphs)  [46]  and pALC14 (for pleomorphs)  [47] , linearized with AscI or NotI respectively, prior to transfection. RNAi target gene fragments were selected based on default settings of the RNAit software  [48].
Northern blotting and transcriptome analysis For northern blotting, RNA preparation and analysis was carried out as described by  [49]. For the transcriptome analysis, RNA was purified using the RNeasy purification kit (Qiagen) and sent to BGI Hong Kong. Quality of sequence data provided by BGI was assessed using FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). No additional processing of the primary data was required.
Protein crystallization, data collection and structure determination Crystals of the KLC2-TPR [A1-B6] fragment were obtained by the vapour diffusion method at 290 K in sitting drops using equal amounts of protein and reservoir solution. The crystallization solution consists of 0. 5 M K2HPO4/Na2HPO4 pH 7. 5, 12% PEG3350 and 1mM Sarcosine and the stock solution of the protein is at 5 mg ml -1. Crystals were transferred briefly to a cryo-protectant composed of reservoir solution supplemented with 25% glycerol and frozen in liquid nitrogen. Diffraction data were collected at 100 K on beamline PROXIMA-2 at the SOLEIL Synchrotron. Crystals diffract up to 3. 4 Å and belong to the monoclinic space group C2 with three molecules in the asymmetric unit. X-ray data were integrated and scaled using XDS  [23].
The structure was determined by molecular replacement with PHASER  [24]  using as a search model the TPR [B1-B6] fragment from the mouse KLC2-TPR [B1-B6] structure (PDB code 3CEQ). The A1 helix was then manually docked in the extra electron density using the human KLC1-TPR [A1-B6] structure (PDB code 3NF1) as model. Refinement was then carried out using autoBuster  [25]  with the autoNCS option  [26]  and the graphical building was performed using COOT  [27]. It should be noted that the 4 N-terminal residues (190-193) from KLC2-TPR [A1-B6] fragment, as well as the 19 residue-stretch containing the His-tag and the thrombine cleavage site were not modeled due to electron density absence. Also, the 24 residues (421-444) from the non-TPR region between the TPR5 and TPR6, as well as the 5 last C-terminal residues are not modelled due to electron density absence.
No disulfide bridge is observed between Cys441 (not visible in electron density) and Cys474 (visible in electron density). Atomic coordinates and structure factors have been deposited in the PDB (code 5OJF). Crystals of the KLC1-TPR [A1-B5] fragment were obtained by the vapour diffusion method at 290 K in sitting drops using equal amounts of protein and reservoir solution. The crystallization solution consists of 22% PEG400, 10% PEG1000 and 150 mM Phosphate pH 6. 5 and the stock solution of the protein is at 7 mg ml -1. Crystals were frozen in liquid nitrogen. Diffraction data were collected at 100 K on beamline PROXIMA-1 at the SOLEIL Synchrotron. The crystals diffract up to 2. 25 Å and belong to the hexagonal space group P6 2 22 with one molecule in the asymmetric unit. X-ray data were integrated and scaled using XDS  [23].
The structure was determined by molecular replacement with PHASER  [24]  using as a search model the TPR [A1-B4] from the KLC1-TPR [A1-B6] structure (PDB code 3NF1). The fifth TPR repeat was then manually docked in the extra electron density using COOT  [27]. Refinement was then carried out using Phenix  [28]  and the graphical building was performed using COOT  [27]. It should be noted that the 24 N-terminal residues (185-208) from KLC1-TPR [A1-B5] , as well as the 21-residue stretch containing the His-tag and the thrombine cleavage site, were not modeled due to electron density absence. Atomic coordinates and structure factors have been deposited in the PDB (code 5OJ8). For both structures, data collection and refinement statistics are presented in Table  1. Mol-Probity was used for model validation  [29].
Size-exclusion chromatography coupled to multi-angle laser light scattering Protein samples were analysed by Size-Exclusion Chromatography (SEC) coupled with Multi-Angle Laser Light Scattering (MALLS). KLC1-TPR [A1-B5] and KLC2-TPR [A1-B6] were loaded on a Superdex 200 10/300 GL increase column (GE Healthcare) equilibrated respectively with 50mM Hepes pH 8. 0, 80mM NaCl; 5% glycerol, 1mM DTT and 20mM Hepes pH 7. 0, 300mM at a flow rate of 0. 5 ml/min on a HPLC system (Shimadzu). Static light scattering was measured with a MiniDAWN TREOS (Wyatt Technology), protein concentration was determined using an Optilab T-rEX refractometer (Wyatt Technology) and dynamic light scattering was carried out with a WyattQELS (Wyatt Technology). The data were analysed using ASTRA1 6. 1 software (Wyatt Technology).
4 Å resolution with three molecules in the asymmetric unit (PDB code 5OJF; data collection and refinement statistics are listed in Table  1 ). This fragment of KLC2 lacks the flexible linker encompassing the LFP motif. The structure of KLC2-TPR [A1-B6] adopts the common KLC-TPR structural fold composed of 12 α-helices (namely A1 to B6 helices; Fig  2A ). The non-TPR region of 40 residues connecting the TPR5 motif to the TPR6 motif is made up of an α-helix (αn1; residue 410-420), followed by a disordered region (421-444) that is not modeled in the crystal structure due to the absence of electron density. Interestingly, the three KLC2-TPR [A1-B6] molecules make the same crystal packing contact with a 2-fold axis symmetrical counterpart leading to TPR1: TPR1' interactions. The two anti-parallel A1/B1 helices from each molecule are roughly perpendicular to each other (Fig  2B ) and bury a total surface area of 1533 Å 2 involving numerous hydrophobic residues.
Because of this crystal contact, we evaluate the oligomeric state of KLC2-TPR [A1-B6] in solution. SEC-MALLS experiment reveals that KLC2-TPR [A1-B6] is a monomer in solution with a weight-averaged mass of 36. 5 ± 1. 0 kDa (S1A Fig). Thus, this TPR1:TPR1' contact alone is not sufficient to stabilize such a dimer in solution. Structural comparison of KLC2 unbound (KLC2-TPR [A1-B6] ; this study) and bound to its LFP-motif (KLC2-LFP TPR [A1-B6] ; PDB code 5FJY, Table  2 ,  [18] ) shows no overall closure of the TPR domain upon LFP-motif binding, in contrast to what has been observed upon SKIP-WD motif peptide binding (KLC2-TPR [B1-B6] :SKIP WD ; PDB code 3ZFW;  [22] ) (Fig  2C ). Indeed, the average distances between A2 and A5 helices are 31. 5 Å and 31. 3 Å in KLC2-TPR [A1-B6] and KLC2-LFP TPR [A1-B6] (5FJY) structures, respectively, while the distance in KLC2-TPR [B1-B6] :SKIP WD (3ZFW) structure decreases to 28.
A new crystal form of KLC1-TPR confirms a distinct N-terminal capping helix orientation The TPR domain of KLC1 (KLC1-TPR [A1-B6] , residues 205-497,  [21] ) was previously crystallized in presence of its N-terminal His-tag. The 3D structure (PDB code 3NF1, Table  2 ) shows that the His-tag linker folds back and binds into the groove, interacting extensively with A1, A2 and A3 helices. In order to visualise the structure of KLC1-TPR domain without this unnatural ligand bound, we created a new fragment of KLC1 that differs in its N-terminal Histag linker sequence. Due to difficulties in crystallization, this fragment lacks the non-TPR region and the TPR6 motif. Thus, we determined the 3D structure of human KLC1 encompassing the TPR motifs 1 to 5 (namely hereafter KLC1-TPR [A1-B5] ; residues 185-418) at 2.
25 Å resolution with one molecule in the asymmetric unit (PDB code 5OJ8; data collection and refinement statistics are listed in Table  1 ). The structure of KLC1-TPR [A1-B5] is composed of 10 α-helices (namely A1 to B5 helices) adopting the common KLC-TPR structural motif (Fig  3A). The region upstream the TPR domain encompassing the His-tag linker has no electron density and was not modeled. So, and in contrast to what is observed in the KLC1-TPR [A1-B6] structure (3NF1), in the KLC1-TPR [A1-B5] structure, the His-tag linker does not fold back into the groove. However, in the crystal, the KLC1-TPR [A1-B5] molecule makes a tight interaction with one of its symmetrical counterparts. Through a 2-fold axis, both C-terminal helices (αB5) bind into the N-terminal part of the groove of the related molecule (Fig  3B ), covering a total buried surface area of 3196 Å 2.
Acknowledgments Scientists of PROXIMA-1 and 2 beamlines ( Soleil synchrotron, Saint-Aubin, France ) are gratefully acknowledged for their support during X-ray data collection. This work has benefited from the expertise of the Macromolecular interactions measurements Platform of I2BC. Access to the  High Throughput Crystallization Laboratory at EMBL Grenoble  is gratefully acknowledged. We thank  Benoı ˆt Gigant  for useful discussions, as well as for careful reading and comments on the manuscript.
Remote sensing The analysis was performed on freely available satellite imagery at two different spatial scales: 250-m MODIS NDVI product and 30m surface reflectances from the Landsat missions. For the moderate resolution, we used the eMODIS product provided by the United States Geological Survey (USGS) and based on MODIS data acquired by the Terra satellite. The product is a 10-day maximum value NDVI composite  (Jenkerson et al. , 2010)  temporally smoothed with the Swets algorithm  (Swets et al. , 1999). Composites are produced every five days, resulting in six temporally overlapping composites per month. Here we only used the composites for days 1-10, 11-20, and 21-last day of each month. Both the time series of 10-day observations and the maximum annual NDVI value, representing vegetation peak development, were used in the analysis.
Inspection of MODIS multi-annual temporal profiles for the intervention areas permitted us to determine the period of vegetation growth, which roughly ranges from June to September, with maximum development reached in late August. Cloud-free Landsat imagery was selected during this period. Landsat 8 Operational Land Images (OLI) data are available since 2013; before then we had to rely on Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. However, Landsat 7 ETM+ imagery collected after 31/05/2002 has data gaps due to the Scan Line Corrector failure (SLC-off;  Andrefouet et al. , 2003). The issue does not prevent the analysis but has to be properly treated, as explained in the methods section. Although not strictly required, the BACI design benefits from having multiple time observations before and after the time of intervention.
Whereas gathering multiple MODIS observations is straightforward, it is very challenging for Landsat 5 and 7 in these geographical settings where the availability of cloud-free images during the growing season is very limited. For instance, a large data gap exists between  2003 and 2007 and between 2007 and 2012,  when not a single cloud-free image is available in the period of maximum vegetation development. The list of the Landsat images used in the analysis is presented in Table  1. Landsat-based NDVI was computed using the red and near infrared bands of surface reflectance products (USGS, 2016a, 2016b) retrieved from the United States Geological Survey. Largely cloudfree imagery was selected, and the CFmask band of the surface reflectance product was used to mask sparse clouds and cloud shadows.
In summary, MODIS and Landsat-based analyses differ in three aspects: i) the spatial resolution (250 m vs. 30 m), ii) the RS variable used (maximum seasonal NDVI vs. NDVI at a specific, and data availability-driven, date during the season), and iii), the temporal period covered before and after the intervention (up to five years of acquisitions vs. a single acquisition). Finally, to check the consistency of BACI results, VHR imagery from Google Earth (GE) was used for the qualitative and visual evaluation of the restoration interventions. The visual analysis of VHR imagery before and after the intervention date aimed to spot signs of interventions, ranging from signs of tractor ploughing to visible patterns of regular plantations and the growth of new trees. When imagery before the intervention was not available in GE (8 cases out of 15), the assessment of the intervention was performed on the imagery only after the intervention, and was based on a comparison of the vegetation cover inside the intervention area with that of the area outside, with obvious limitations on the possible interpretation.
The ratio r was set for this study to 600. If the impact area had a circular shape, this would correspond to a ratio between the area searched and the impact radius of 24. 5. In the case study, this resulted in an average search radius of 25 km (range = 9-61 km), where similar climatic conditions can be reasonably expected. Second, we use the images acquired in the period before the intervention (Fig. 2A ) to perform an iterative self-organised unsupervised clustering algorithm (ISODATA) with n classes spatially restricted to the search area (Fig. 2B ). With a trial and error process based on the qualitative comparison of the ISODATA classification map and VHR imagery, we set n = 5 in this study. A larger number of classes can be selected if the landscape is more heterogeneous. To fully compare the results gathered with the Landsat and the MODIS analysis, we perform the classification using either Landsat or MODIS data for the two types of analysis, implying that different control sites are selected.
For the Landsat analysis, all the bands in the reflected domain of a single image are used for the classification, whereas for MODIS we follow the approach proposed by de Bie et al. (  2011 ), using the multi-temporal NDVI trajectory instead of the multispectral information. The classification is thus performed on a five-year multi-temporal dataset of 10-day composites ending the year before the implementation of the restoration action. After this classification stage, the fractional class composition of the impact area is computed. Third, we define a generic control as a square spatial window with the same area of the impact site. The population of potential controls is thus formed by all the possible and overlapping win-dows centred on each of the pixels belonging to the search areas. Potential controls that overlap other impact sites or excluded areas (the area subject to environmental conservation in our case study) are excluded.
Treatment of Landsat 7 SLC gaps An estimated 22% of the Landsat 7 scenes is lost because of the SLC failure (http://landsat. usgs. gov/products slcoffbackground. php). The SLC-off effects are most pronounced along the edges of the scene and gradually diminish toward its centre. The precise location of the missing scan lines varies from scene to scene. Therefore, it is difficult to anticipate the fraction of missing data for individual impact areas. With our test cases, the fraction of missing data varied between 0% and 40% and operated as a random subsampling with no expected consequences on the following BACI test. In the presence of this SLC problem, the affected pixels were considered as belonging to an additional land cover class, thus contributing to the similarity measure described above.
BACI analysis A significantly negative BACI contrast (i. e. improvement in NDVI with respect to controls after the intervention) was detected in five and four out of 15 sites using MODIS and Landsat data, respectively. For the majority of sites, the (null) hypothesis of no change could not be rejected. For three sites, the contrast was indeed positive, i. e. there was a relative decrease in NDVI in the restoration area. Focussing on the sites for which a significant BACI effect was detected, the average relative contrast is -20% and -27% for MODIS and Landsat data, respectively. Considering NDVI as a rough approximation of the fractional vegetation cover, these numbers translate into a significant improvement in the vegetation cover with respect to the controls. As an example of the data used for the BACI analysis, impact and control averages are shown in Fig.
However, the P-value of Landsat (0. 058) is not far from the threshold (0. 05) used to reject the (null) hypothesis of no change. As a result, the change detected using Landsat data has a lower confidence level (P < 0. 10). Other minor differences (i. e. not leading to different test outcome) between the results of the two types of analysis refer to different magnitudes of the P-value. The P-value of MODIS is generally lower than that of Landsat. Fig. 4  shows the P-value of the two types of analysis vs. the absolute value of the relative BACI contrast. Both types of analysis show a reduction in the P-value with increasing absolute value of the relative contrast, as the test essentially builds (also) on the magnitude of the BACI contrast. However, the MODIS P-values are mostly lower than those of Landsat for similar relative contrasts.
Therefore, the multiple temporal sampling that can be achieved using MODIS data appears to be instrumental in increasing the significance level of the test with respect to the single time analysis of Landsat. This is likely due to three reasons: i) increased sample size for MODIS analysis, ii) better representation of the overall vegetation cover offered by the maximum NDVI with respect to the single date NDVI, and iii) reduced dependency of MODIS on a specific year and time. Concerning the latter, with the MODIS set-up we analyse the control/impact differential behaviour in a multi-year time span, making it less sensitive to possible year-specific peculiarities that may affect the single-year high-resolution Landsat set-up (see Fig. 3 ). It is noted that the same multiple-period design can be applied to high-resolution freely available data in geographical settings with a higher availability of cloud-free Landsat imagery, or when analysing more recent projects that can exploit the more frequent availability of Landsat 8 imagery and other recently available instruments (e.
Conclusions For the first time, a before/after control/impact (BACI) design was applied to RS data to evaluate the biophysical impact of restoration projects. Large agreement was found in the statistical test outcomes using either MODIS or Landsat data. The availability of frequent MODIS observations makes the data of this instrument well suited to the most robust BACI design, exploiting multiple controls and multiple observations before and after the intervention. The use of Landsat data in our test case study was limited by the poor availability of cloud-free imagery, compelling the application of a single-time BACI design and resulting in generally lower confidence (i. e. higher significance level, P-value) of the test results. The analysis of more recent intervention projects will benefit of the availability of more frequent satellite observations from Landsat 8 and Sentinel 2 satellites.
Acknowledgements We would like to thank  Moustapha Bassimbé Sagna  for the support during the field missions and the  CNRS Human Environment Observatory (Tessekeré, Senegal)  for the financial support for field missions. The work was funded by the  Administrative Arrangement between the European Commission DG DEVCO  and the  JRC for Technical  and scientific support to agriculture and food and nutrition security sectors" ( TS4FNS2 , ref. 33272).
txt file did not have the same name in any case; • the data coming from the nodes with the same name completely stopped the operation of the system. Accordingly, the structure created by ARPANET was morally outdated and the need for the establishment of a new system emerged. In 1983, researcher of the University of California (USA) Paul Mockapetris designed the Internet Domain Names System and developed DNS, which is currently being used, together with researcher of the Information Sciences Institute (USA) Jon Postel. From January 1985, the registration of common domain names started, and in the same year for the first time, the top-level country codes in Latin were defined for the United States (. us), United Kingdom (. uk) and Israel (. il). In November 2000, sponsored the top-level domains (sTLD) (.
sumgait for big cities as Baku, Ganja, Sumgait and others is one of very important steps. This can motivate the population of these regions to access the virtual world. Furthermore, unofficial competition would occur among regional countries in this regard. This step may also prevent the problems that exist in the registration of domain names in Azerbaijan. DNS is a distributed database. Its structure is hierarchical. This structure allows locally controlling separate sections of the network and at the same time, these segments become accessible over the network due to the use of client-server mechanism. In order to ensure reliability and efficiency for the system, DNS replication and caching is used. So-called DNS Server applications are functioning in the server side of the client-server mechanism.
These servers contain the data about the domain names and the Internet Protocol (IP) address of all nodes in the segment. The client function is activated in all machines, and prepares and sends DNS requests to the server and delivers the obtained information to the user. The data is hierarchically stored in the DNS servers, and each part of the hierarchy is a subpart of the previous one (Figure  1 )  [4]. Each domain is the root of subsequently generated subdomain. A symbolic dot (". ") is putin the highest part.
g. , Electronic Frontier Foundation, eff. org). since 1996, it has been accessible for all commercial organizations in. net domain; •. int -for the registration of domain names of international organizations (e. g. , NATO, nato. int). Each domain or directory can be divided into smaller parts, which are called subdomains. The control of subdomains in DNS can be delegated to another institution. All domains and subdomains are recorded in the indexed for in the database of domain names. Thus, the domains at root zone or any other top-level unifies all the paths going to subdomains  [5]. A new zone is generated together with each subdomain (Figure  2 ).