dataesr/scientific-paragraphs-categorization huggingface dataset:
We first spatially overlapped the Landsat images with the 250 m resolution grid and calculated the standard deviation of each set of Landsat pixels falling within each grid cell of 6. 25 ha (i. e. , approximately 64 Landsat pixels) for 2016 and 2001, separately. We then calculated pixel-wise the difference between the standard deviation values in 2016 and 2001. This allowed us to summarize the spatial NDVI variability change of the high-resolution Landsat data at the MODIS pixel size and bring this metric to a common spatial sampling scale with the other two metrics. This third landscape metric was calculated as follows (Figure  4c  shows a schematic representation of the calculation of NDVI spatial ): NDVI spatial (Y t , Y 0 ) = σ(Y t ) -σ(Y 0 ) (3) σ(Y 0 ) = 1 n n j=1 Y 0, j -Y 0 2 (4) where Y 0 and Y t are the set of high-resolution pixels within a coarse-resolution cell in the first and last year images, respectively, and n is the number of high-resolution pixels that fit within a coarse-resolution cell.
3. The missing data gaps in the time series were temporally interpolated with the average value of the previous and next months and the monthly data was aggregated to retrieve the annual total precipitation (mm) for each year. 4. The Inverse Distance Weighting (IDW) algorithm implemented in the Interpolation Plugin of the Quantum GIS (QGIS) software  [120]  was then applied to the annual total precipitation data to obtain spatially continuous annual spatial interpolations over the whole study areas at a 5 km spatial resolution. The Zonal Statistics Plugin  [120]  was then used to extract the total annual precipitation values for the 30 year period for each CU centroid. 5. To evaluate the rainfall conditions in the two analyzed years (2001 and 2016), we used the climatological normal (i. e.
Figure3. MODIS data collected and preprocessing steps. 1 The original data derives from 5 MODIS tiles h12v10, h12v11, h13v09, h13v10, h13v11, which have been cropped to the extent of the study areas and mosaicked to a single image mosaic per date (i. e. , 23 mosaics per year). 2 The final annual time series start and end on the day-of-year 1 and 353, respectively.
Data analysis This paper presents measurements of the p-p, p-p, p-Λ, and p-Λ correlation functions in high-multiplicity pp collisions at √ s = 13 TeV performed with ALICE  [47, 48]. The high-multiplicity trigger selected events based on the measured amplitude in the V0 detector system  [49] , comprising two arrays of plastic scintillators at 2. 8 < η < 5. 1 and -3. 7 < η < -1. 7. The threshold was adjusted such that the selected events correspond to the highest 0. 17% fraction of the multiplicity distribution of all INEL> 0 collisions. In such events, an average of 30 charged-particle tracks are found in the range | η |< 0. 5  [50] , which constitutes an increase by a factor of about four with respect to the minimum bias data sample  [42]. The V0 timing information was evaluated with respect to the LHC clock to distinguish collisions with the beam pipe material or beam-gas interactions.
After 10 seconds had elapsed, the experimenter again held the platform while obscuring a marker placed on the platform, which represented the end of the trial. Each subject was given one minute to rest between tests. A 3 D motion capture system (ViconT10, Oxford, UK, 100 Hz) recorded data for 34 passive markers (14 mm) to define "Plug-in gait" model. To measure oscillations on the platform and compute its center, four markers were positioned in each upper corner. Two trials were performed for each subject. A filtering procedure with a second-order Butterworth filter (6 Hz) was applied to the markers data. Center of Mass (CoM) was extracted from Nexus and displacement of CoM was computed with Matlab. About back stiffness, four stabilization segments were analyzed according: upper back (C7-T10), lower back (T10-sacrum), right thigh, left thigh.
No bacterial prophylaxis with quinolone or other antibiotics was used. Primary refractory AML was defined as a failure to achieve CR1/CRi1 after induction chemotherapy, and such cases were not included in the present study. Patients who received multiagent chemotherapy as a postremission strategy were also excluded. Written informed consent was obtained from all patients in accordance with the Declaration of Helsinki, allowing the collection of clinical data in the anonymized French Toulouse-Bordeaux DATAML registry. Institutional review board approval (DR-2015-529) was received. Cytogenetic risk classification was defined according to the UK Medical Research Council classification. 7 Intensive induction chemotherapy was based on daunorubicin at a daily dose of 60 to 90 mg/m 2 for 3 days or idarubicin at a daily dose of 8 to 9 mg/m 2 for 5 days, together with continuous intravenous infusion of cytarabine at a daily dose of 100 to 200 mg/m 2 for 7 days.
Acknowledgments The English in this document has been checked by at least 2 professional editors, both native speakers of English. For a certificate, please see: http://www. textcheck. com/certificate/TFq6bn. The authors thank all the members of the Ga ël  Adolescent Espoir Leuc  émie association, the  Toulouse Cancer Sant é Foundation , and the  FONROGA Foundation  and the " Canc érop ôle Grand Sud-Ouest et le Groupement Interr égional de Recherche Clinique et d'Innovation Sud-Ouest Outre-Mer" for the "Appel à Projets Interr égional Cancer 2016  ( API-K ). " This study was funded by the data management unit of  Toulouse University Hospital  and the  Cancer Pharmacology of Toulouse Oncopole  and  Region  project ( ANR-11-PHUC-001 ), enabling e-CRF.
5-2. 5 OD units (individual strains) and up to 2-3. 5 (Salmonella pools). We used the 42 sera at their α dilutions to generate a dataset (one technical replicate) in a matrix-like fashion (Fig. 1 ), where the rows represents the different sera (coded from 1 to 42) and the columns the Salmonella strains (coded from A to BC). The results of the background signal assessment of the secondary antibody (CRs of 0. 05-0. 1 see Supporting Information S3, Sect. 1), and of the "primary antibodies" (the dilution-dependent CRs of 42 sera against an irrelevant proteins, resulting 0. 13 ± 0. 07, see Supporting Information S3, Sect. 2), indicate that the signal -to -noise ratios of the cells in Fig. 1  range from 1 to 25. Within every row, we used the normalization with base 100 of the CRs (NR%) according to Eq.
(  1 ): (1) NR% = CR of a given well CR of the well coated with the immunogen × 100 (c) Upon re-calculating the NRs% against a common reference strain, the specificity profiles of a given mixture can be predicted and experimentally verified. (d) Final step of serum mixtures optimization aiming at the simplification of their composition and fine-tuning of their specificity profiles. The values presented in this scheme are just explanatory of the general workflow, and thus they do not correspond to the actual dataset. which imposes NR% = 100 for the strain used as immunogen (e. g. intersections 5-H and 42-BC in Fig. 1 ), and values ranging from 1 to 100 in all of the other combinations. Technical replication of the specificity profiles shows a mean for the coefficients of variation percent (CV%) of 7% ± 5%, with few instances showing CV% up to 20% (Supporting Information S3, Sect.
4). In few instances, due to the technical reproducibility, and the molecular similarity in terms of lipopolysaccharides (LPS) and membrane proteins between some strains, the MR localize at the intersection with strains that were not used as the immunogen, thus yielding NRs% slightly exceeding 100 (see for example the intersections 7-M, and 14-AS in Fig. 1 ). Each row in Fig. 1  is therefore colour coded as a "heatmap" of the NRs%, allowing for a visual comparison between sera specificity for the Salmonella strains. The heterogeneity of the NR% values in Fig. 1  reflects the specificity profiles emerging from the use of immunogens with difference at both qualitative (strain used) and quantitative (single strain or pool) level, and the biological diversity of rabbit immune repertoires. To highlight differences in the NR% dataset (54 responses per serum), one can run ANOVA pairwise comparisons either considering the individual sera as independent entities (42 sera stemming from 42 rabbits, scenario "A", Supporting Information S4-a,c), or by accounting for the biological replicates (2 rabbits for each one of the 21 immunogens, scenario "B", Supporting Information S4-b,d).
We then introduced the arbitrary descriptor γ%, which is the average of the NRs% of a sera used at the α dilution against the 54 individual strains, and its standard deviation (γ% SD ). This descriptor is thus "orthogonal" to β% and can be related to the intrinsic specificity properties of the sera and therefore their capacity to recognize one, few, or multiple strains in a defined range of NRs%. γ% ± γ% SD is therefore a measure of sera "inclusivity" against the set of Salmonella strains (the greater the γ% ± γ% SD, the better the inclusivity), and "exclusivity" against the set of E. coli and the other bacteria species (the smaller the γ% ± γ% SD, the better the exclusivity). To provide a visual insight on which are the "theoretical limits" of γ% ± γ% SD values as a function of the possible distributions of NRs%, let's consider a hypothetical and simplified dataset with 54 sera, each one yielding NRs% of either "0" or "100" for every one of the 54 Salmonella strains (Supporting Information S4b).
g. see the interceptions 41-BC and 42-BC for the two sera directed against S. bongori V, Ad598), while all the other NRs% range between 1 and 100. The NRs% are colour-coded from red (towards 0) to yellow (50), to green (100). The bottom row reports the β% values for every strain, while the two rightmost columns show the γ% ± γ% SD for every serum (see "Sera ranking by descriptors"). Graphical representation of γ% ± γ% SD for every serum (b). within the ranges imposed by the theoretical distribution (Fig. 2c ). From this dataset, γ% values were used to rank sera as single-strain specific (those in Fig. 1  with 11 < γ% > 30), multiple-strain specific (30 < γ% > 50) and those with high inclusivity (γ% > 50). We rapidly recognized that a ranking based only on γ% values can consider good sera also those having poor α values (e.
bongori. There is a good agreement between the experimental and predicted NR% values, resulting in artificial and experimental datasets that does not show statistically-significant differences between them (see Fig. 3b  and Supporting Information S6, Figure  S6b  and Table  S6b ), and that highlight which strains benefit from MIX design (e. g. see the comparison between serum 14 and MIX#4 in Fig. 3a ). Interestingly, the volumetric ratios for MIX generation were effective also at the preparative scale, meaning the blending of neat sera in a MIX that is then diluted at the α value, instead of combining the 10X diluted sera (used for titer and specificity assessment) at the final dilution. Indeed, the comparison of MIX#3 produced from diluted constituents or from neat sera (MIX#3 (exp. α=8) and MIX#3 neat (exp.
Serum mixtures optimization. Some considerations prompted us to investigate whether we could further optimize MIXs composition by reducing the number of sera (3-5), and tailoring their dilutions. We produced MIX#5 with 3 sera, (5, 12, and 27), MIX#6 with 5 sera (15, 17, 19, 22, and 26), and MIX#7 with 4 sera (27,  29, 32 and 33). See Supporting Information S6 for their composition. Their specificity evaluation (Fig. 3a, d ) clearly reports that MIXs#5-6-7 display harmonized specificity profiles, with MIXs#7 showing γ% ± γ% SD greater than those of the best sera (p < 0. 001), while MIXs#5 and MIXs#6 performing better than 12 and KPL (p < 0. 001), as revealed by pairwise comparisons (see Tables in Supporting Information S6). Upon technical replication, one can see that the inclusivity datasets of the MIXs are undistinguishable, even upon addition of irrelevant sera as a way to "foster" possible background increases due to the number of constituents in the MIXs (p < 0.
The resulting dataset of 64X8 colorimetric responses was visually inspected (Fig. 4b ) and subjected to software-based optimisation (sDoE Supporting Information S8), by having as goal the individuation of the optimal combinations of inclusivity and exclusivity. By visual inspection of the heatmaps in Fig. 4b , there are many combinations fulfilling one of these two requirements, such as greatest inclusivity (those with high volumetric ratios of serum 12), greatest exclusivity against E. coli (those with low volumetric ratios of 27) or greatest exclusivity against the other genus (those with low volumetric ratios of 5). There are only two combinations that maximize the specificity requirements, both of them employing 12 at α = 256 and 5 at α = 192, but slightly differing for 27 dilutions, (α = 256 or 128 for the combinations at the intersection C1 and G1 in Fig.
Conclusions We have presented a rational strategy to develop mixtures of antibodies able to maximize and harmonize the specificity profiles against 54 targets (in this case Salmonella strains) that share different degrees of similarity among them and between closely related analytes (E. coli strains). This strategy is based on the production of a sufficient number of sera to cover the different features of the analytes (antigenic properties), and on the evaluation of their relative specificity profiles emerging from indirect ELISA assays. Rational parametrization of the resulting datasets allows predicting the responses of mixtures of different sera towards the selected analytes, where few sera resulting from immunisation against pools of Salmonella strains, thus showing broad specificity, can serve as a "base" for a further specificity tuning, thanks to the addition of sera with narrower specificity profiles (e.
In general, this strategy is very suitable for the development of bioanalytical assays where a multitude of analytes displaying molecular similarity need to be discriminated, thus requiring a signal threshold. These are the typical requirements of ELISA-and flow cytometry-based detection of drugs belonging to different class of actions, or proteins having immunomodulatory effects such allergens and gluten fractions. This strategy generates datasets that are in between what starts to be too large to be analysed by mere man-made operations in a spreadsheet software, and what is too little for the mathematical models and software necessary for big-data handling (e. g. next-generation sequencing). Nonetheless, for this size of datasets, there are several mathematical and statistical models that can be employed to optimize and find trade-offs between the conflicting objectives of inclusivity and exclusivity maximization of a given mixture, such as multi-objective optimization in closed loop fashion  39  , by taking advantage of very rapid problem description, computational prediction, and experimental verification.
Scheme 1. General workflow for the design and preparation of serum mixtures with custom specificity profiles. (a) Sera production by rabbit immunization with individual or pooled Salmonella strains, and the preliminary characterization by colorimetric indirect ELISA. This yields an ensemble of specificity profiles, one per serum, which are expressed after a simple data treatment as heatmaps of the normalized colorimetric responses (NRs%). (b) Conversion of the titer values and colorimetric dataset into descriptors (α, β%, γ% ± γ% SD ), allows ranking the sera.
Acknowledgements This work was funded by the  Walloon Region  by the CWality projects # 1510440  " Food on flow " and # 1710130  "Meatscan". R. Marega  thanks  Nawel Al Achab  ( Tecan Belgium ) for the possibility of using a digital dispenser (model D300E) necessary to produce the 64 mixtures required for the complete factorial design, and  Prof. Manuel Lopez-Ibanez  ( University of Manchester ) for the ongoing mathematical formalization of the proposed optimization strategy.
Scheme 1. 1 Scheme 1. General workflow for the design and preparation of serum mixtures with custom specificity profiles. (a) Sera production by rabbit immunization with individual or pooled Salmonella strains, and the preliminary characterization by colorimetric indirect ELISA. This yields an ensemble of specificity profiles, one per serum, which are expressed after a simple data treatment as heatmaps of the normalized colorimetric responses (NRs%). (b) Conversion of the titer values and colorimetric dataset into descriptors (α, β%, γ% ± γ% SD ), allows ranking the sera.
Strains and Culture Conditions The S. apiospermum wild-type (WT) strain used in this study, deposited at the BCCM/IHEM culture collection (Brussels, Belgium) under the accession number IHEM 14462, was isolated in 1998 from a sputum sample from a CF patient in Tours, France. As described below, a non-homologous endjoining-deficient strain (Dku70) was obtained from this WT strain, and was subsequently used to generate sidD disruptants. Strains were maintained by regular passages on Potato Dextrose Agar (PDA) plates supplemented with 0. 5% chloramphenicol. For cultivation of the Dku70 parent strain and the DsidD disruptants, phleomycin (20 µg/ml) and hygromycin B (50 µg/ml) were also added to the culture medium, respectively, in order to maintain the selection pressure.
Mass spectra of selected ions were collected at a 1 Da isolation width and 20-25 V collision energy. Final spectra represented an average of 16 or 32 acquired scans. Data were processed using the DataAnalysis software (v. 4. 1, Bruker Daltonics) and siderophores were annotated in CycloBranch (v. 2. 0. 8) against our databases  (Pluhaćěk et al. , 2016; Novaḱ et al. , 2020).
Disruption of the sidD Gene in S. apiospermum Because of the high frequency of non-homologous recombination events in S. apiospermum, all our attempts to produce a DsidD mutant from the wild-type strain failed (data not shown). Therefore, we first produced a mutant strain deficient for the non-homologous end joining (NHEJ) by disruption of the KU70 gene encoding one of its protein subunits. Almost 20 colonies growing in the presence of 20 µg/ml phleomycin were obtained after transformation of the WT strain IHEM 14462 with the KU70 deletion cassette. The genotype of monospore isolates obtained from these transformants (Dku70) was verified by Southern blot. As illustrated in Figure  1B , a band of the expected size (5. 6 kbp) was visualized for PV16-A, B, and C monospore isolates after hybridization with the probe, instead of the 3-kbp band observed for the WT strain, thus demonstrating the successful disruption of the KU70 gene.
Siderophore Synthesis in S. apiospermum The S. apiospermum WT strain and DsidD mutants were examined for the production of siderophores by the highresolution MALDI MS analysis. This analysis was performed on lyophilized filtrates obtained from the WT strain and DsidD mutants (M1, M2, and M3) grown in minimal YNB medium under iron-sufficient and/or iron-depleted conditions. Analysis of the extracellular siderophores performed on culture filtrate from the WT strain revealed one possible candidate against the siderophore/secondary metabolites database, namely, N a -methylcoprogen B (C 34 H 56 N 6 O 12 ) both in desferri-(m/z 741. 403, [M+H] + ) and ferri-(m/z 794. 314, (M+Fe-2H) + ) forms (Figure  2A ). Characterization of these ions was further confirmed by their tandem MS fragmentation patterns (Figures  2C, D ) compared with the literature  (Antelo et al.
Patients scoring above 45 were considered as having positive attitude, those scoring below 45 as having negative attitude while patients scoring exactly 45 were considered as having ambivalent attitude towards ECT. Inclusion Criteria: 1. Patients who had previous experience with electroconvulsive therapy. 2. Patients 18-60 years of age. 3. Both male and female patients. 4. Patients who could understand Pashtu, Urdu or English. Exclusion Criteria: 1. Those who were grossly psychotic or acutely manic with no insight. 2. Those under the influence of or being treated for substance abuse. Data Analysis: The data gathered was analyzed using the Statistical Program for Social Sciences (SPSS), version 20. For the variables of continuous type like age, the Mean with ±S. D was calculated. For the variables of categorical type like gender, marital status, educational status, status of employment and attitude towards ECT, frequencies and percentages were calculated.
We then perform detailed modeling of microwave emission from rapidly spinning nanoparticles from both C-rich and O-rich AGB envelopes using the grain size distribution constrained by rotational disruption. We find that spinning dust emission is dominant over thermal dust emission at frequencies below 100 GHz. We attempt to fit the observational data of AME using our spinning dust model and demonstrate that spinning dust can reproduce the observed AME in six AGB stars. Finally, we discuss that microwave emission from spinning dust in AGB envelopes could be observed with high-resolution upcoming radio telescopes such as ngVLA and ALMA Band 1. This would be a major leap for understanding AGB envelopes, formation, evolution, and internal structures of dust. Observations would help to distinguish the carrier of AME from comparing C-rich to O-rich stars, because PAHs are formed in C-rich AGB stars while silicates are formed in O-rich stars.
It is worth to note that the global purpose of this work is to demonstrate the spinning dust is one possible explanation for the AME detec-tions in AGB envelopes so that let us now use our model calculated for IRC +10216 and IK Tau to fit with the observational data of two C-rich and O-rich AGB stars, respectively. To fit the data, we vary three parameters S max , β and η while fixing other physical parameters until we obtain the best-fit models. In purpose of showing the whole SED, we combine with the best model of the thermal dust emission provided by  Dehaes et al. (2007) , which were modelled by DUSTY code  (Ivezic et al. 1999 ) with M d/g = 0. 005. Figure  10  shows our best-fit models to observational data for three C-rich (upper panel) and three O-rich (lower panel) stars with given the corresponding set of dim parameters 3 in the caption.
Simultaneously, dust continuum observations at comparable resolution will be useful to map the dust shell and hence spatially correlating it with the gas emissions. Multiband observations of AGB stars from VLA with frequencies from 1-40 GHz may help in understanding the dust distribution by investigating the fluxes obtained in these frequencies and fitting SEDs. VLA plays an important role in covering the regimes of frequencies less than 40 GHz (see Figure  10 ) at much higher resolution than that of available data in literature. Therefore, a combination of VLA and ALMA dust continuum observations in some of the bright AGB shells will help to pin down the dust characteristics using our dust models. VLA and ALMA also provide unique opportunities to map high resolution dust continuum and line polarization.
We thank the anonymous referee for helpful comments that improve the impact and the presentation of this paper. This work was supported by  NASA  through award # 06 0001  issued by  USRA ,  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education  ( 2017R1D1A1B03035359 ), and  National Science Foundation  Grant -1715876. A. S acknowledges the financial support from the  NSF  through grant  AST-1715876.