dataesr/scientific-paragraphs-categorization huggingface dataset:
The decay rates in pairs of the lightest neutralinos, possible candidates for Dark Matter, are instead suppressed in both reference points. As a whole, supersymmetric decays are responsible for 12% and 15% of the Z width in the representative points BM I and BM II, respectively. Once our representative configurations are set, we carry out a full Monte Carlo event simulation at the LHC, for a center-of-mass energy √ s = 14 TeV. Hard-scattering signal events are generated with MadGraph5 aMC@NLO, the matrix elements being convoluted with the NLO set of NNPDF 2. 3 parton densities. The production cross section is then σ(pp → Z ) 120 fb for both benchmarks. Parton showers and hadronization are simulated by means of the Pythia 8 program  [62]  (version 8. 2. 19), and the response of a typical LHC detector is modelled with the Delphes 3 package  [63]  (version 3.
Acknowledgments JYA thanks Altan C ¸akır regarding the usage of the Snowmass background samples,  Florian Staub  for the implementation of the model in SARAH and Özgür S ¸ahin for providing help with the ThePlotting software. MF acknowledges the  NSERC  for partial financial support under grant number  SAP105354. The work of BF is partly supported by  French  state funds managed by the  Agence Nationale de la Recherche (ANR) , in the context of the  LABEX ILP  ( ANR-11-IDEX-0004-02 ,  ANR-10-LABX-63 ).
Methods Data selection and processing. Precipitation. Monthly mean precipitation grids at 1° × 1° resolution from June 1950-May 2017 are used for this study  25. Grids were aggregated on a June to May annual year to better preserve a) monsoonal precipitation and b) ENSO -driven precipitation on an annual scale. For example, the designation of 1950 is defined by June 1950-May 1951. The aggregated monthly mean precipitation grids were multiplied by 365. 25/12 to produce the annual total precipitation at each grid cell. To ensure commensurability in the mean trends, LR was conducted for the 1950-2016 period for both June-May and January-December annual year designations (Supplementary Fig. S4 ). Slope coefficients have a correlation of 0. 99 on the global scale. Population. Population data was obtained from the 2015 UN-adjusted population count v4 dataset  38  at a 1 km resolution.
Rainfed agriculture. Rainfed agriculture land types were extracted from the Anthropogenic Biomes of the World v1  30. Data is at a 10 km resolution. Anthropogenic biomes with the descriptor of rainfed (Rainfed Villages, Rainfed Mosaic Villages, Residential Rainfed Mosaic, and Populated Rainfed Cropland) were chosen as representative of rainfed agriculture land types. Descriptions of all land types in the dataset can be found in ref. 39. Precipitation data quality control. To account for differences in results arising from the use of alternative gridded precipitation products, confirmatory analysis was pursued. We compared the PREC/L at a 0. 5° × 0. 5° resolution  25  and CRU TS 4. 01 monthly precipitation  40  datasets on a June-May annual year from 1950-2011. We find that the correlation of the annual total time series is high for large areas of the globe (Supplementary Fig.
S5 ), with low correlation over parts of North and Central Africa, upper northern latitudes, and western South America. Differences in these areas likely arise due to spare station coverage and differing interpolation processes  41. We find that the correlation of global annual mean precipitation between the products is r = 0. 95, showing high agreement. To ensure that regression results are not biased due to year-to-year persistence in the precipitation data, we applied autocorrelation  42, 43  , finding significant lag 1 autocorrelation in only 7. 7% of grid cells (details found in Supplementary Information and Supplementary Fig. S6 ). Trend calculation with wild bootstrap. Trends are assessed through Quantile Regression (QR)  24, 44  using the quantreg package for R  45. This work uses a linear QR model as: τ β β | = + + τ τ τ Y x x ( ) (1) 0 ( ) 1 ( ) ( ).
Supporting monolayer studies showed that the addition of cytochrome c was disrupting membrane packing accompanied by changes in the elasticity modulus. The morphological changes were of biological significance because CL domains are hypothesized to form in select regions of the membrane, specifically in areas of high membrane curvature  (59). For instance, Renner and Weibel  (16)  reported that CL domains localized in  or MLCL (B). Cytochrome c was fully reduced with the addition of ascorbate (90 mM, solid red line). Full spectra (200 -700 nm) were obtained at a pH of 7. 4 in the presence of glucose oxidase (15 units), glucose (5 mM), and catalase (5 units) to ensure O 2 and H 2 O 2 free conditions. Blue shift of the Soret band at 400 nm (data not shown) and decreases in the ␣-heme bands at 518 and 550 nm (A and B) are characteristic of oxidation of the Fe(II) center to Fe(III) with increasing lipid concentrations.
For comparison, cyt c was fully oxidized after the final addition of lipid using 3 mM K 3 [Fe(CN) 6 ] (dashed red line). C, the relative change in absorbance at 550 nm, defined as the absorbance at a specific lipid concentration/absorbance of the fully reduced complex, was determined and plotted as a function of lipid concentration (M). Data are average Ϯ S. D. from three independent experiments. regions of high curvature induced with microchambers using an Escherichia coli model. Thus, proteolipid domains in the inner mitochondrial membrane may be localized to regions of high curvature induced by strong interactions between cytochrome c and CL. We did not observe proteolipid domain formation with the intrinsically disordered protein ␣-synuclein. The rationale for using ␣-synuclein was based on evidence that it can interact with mitochondria to diminish complex I activity  (60).
Conclusion The data demonstrate that CL-specific microdomains cannot be effectively isolated via classical biochemical detergent extraction techniques. More importantly, membrane domains were formed in GUVs and newly constructed GMVs with cytochrome c. These domains were sensitive to a reduction in CL concentration but not substitution with MLCL. At a molecular level, the impairment in domain formation with the loss of tetralinoleoyl-CL was due to changes in the Gibbs-free energy of lipid mixing. Collectively, the results have strong implications for the ongoing debate about CL concentration and acyl chain composition and their role in regulating membrane biophysical organization. All other reagents and solvents (HPLC grade) were purchased from Sigma or Fisher Scientific. Lipid mixtures were handled under low light conditions and fresh stocks of lipid mixtures were used for each experiment to minimize oxidation.
Proteolipid domains are regulated by CL concentration Microscopy, Thornwood, NY). The Texas Red DHPE probe and Alexa 594 secondary antibody were excited by a diode laser at 559 nm, whereas the NAO probe was excited with an argon laser at 488 nm. A diode laser at 633-635 nm excited the Alexa 647 primary antibody used for cyt c immunofluorescence. All acquired images were of GUV equatorial cross-sections. Lipid microdomain and colocalization analysis was conducted using NIH ImageJ software  (74). After background subtraction, the average area occupied by the fluorophore was determined by adjusting the image threshold and analyzing particles that were Ն0. 1 m 2. We selected this threshold, because an area Ͻ0. 1 m 2 resulted in particles/domains that could not be accurately measured. The average vesicle diameter was determined by measuring the horizontal distance between the membrane edges.
For comparison, cyt c was fully oxidized after the final addition of lipid using 3 mM K 3 [Fe(CN) 6 ] (dashed red line). C, the relative change in absorbance at 550 nm, defined as the absorbance at a specific lipid concentration/absorbance of the fully reduced complex, was determined and plotted as a function of lipid concentration (M). Data are average Ϯ S. D. from three independent experiments.
All the PCR products were overlapped at least 200 bp to ensure that they were amplified from the same genome. Each PCR fragment was purified using a MEGA Quick-spin™ Kit (Intron, Korea) and cloned into the pGEM-T easy vector (Promega, USA) according to the manufacturer's instructions, followed by transformation into Escherichia coli DH5α. The clones of each fragment were completely sequenced by a commercial company (Genotech, Korea). The resultant sequences were assembled using DNA Star v. 5. 02 (Lasergene, USA) and have been submitted to GenBank database under the accession numbers listed in Table  3. Sequence and phylogenetic analyses. The complete nt sequences and the deduced amino acid sequences were aligned using the ClustalX2 program and Geneious methods in Geneious Pro 8 and compared with those of previously reported isolates; i.
e. , the JAN (Japan), CHN, FJ, Xinjiang, and CZ (China,  Xiang et al. , 2008b) , R-TW82 and C-TW20 (Taiwan,  Knierim et al. , 2013) , N (France,  Lecoq et al. , 1992) , Sq/2003/7. 2, Sq/2004/1. 9, and Sq/2005/9. 2 (Spain,  Kassem et al. , 2013). The MABYV isolates, CHN and TW1, were used as outgroups (Table  2 ). Nucleotide and deduced amino acid sequence similarities were analyzed using AlignX implemented in the Vector NTI Suite (Invitrogen, Carlsbad, CA). Pairwise genetic distances and pairwise synonymous (dS) and nonsynonymous (dN) substitutions were analyzed by Kimura's two-parameter method  (Kimura, 1980)  and the Pamilo-Bianchi-Li method  (Li, 1993; Pamilo and Bianchi, 1993) , respectively, using the MEGA6 program  (Tamura et al. , 2013). The phylogenetic relationships of the CABYV sequences were analyzed by the maximum likelihood (ML) method in MEGA 6.
In ML analyses, the phylogenetic trees were constructed using best fit nucleotide substitution models (GTR+G+I for full-length genome and K2+G for IR region and 3' UTR) and best fit amino acid substitution models (JTT+G for all protein regions). Bootstrap values were calculated using 1,000 random replication. All positions containing gaps and missing data were eliminated. Geneious Pro 8 software was used to calculate the percentage nucleotide and amino acid identities. Recombination analyses. Recombination events on the full-length sequences of 33 CABYV isolates and 2 MABYV isolates were analyzed using RDP, GENE-CONV, BootScan, MaxChi, Chimaera, SiScan, and 3Seq methods implemented in the RDP4 software (Recombination Detection Program, ver. 4) with default settings and a Bonferroni corrected P-value cut-off of 0.
Recombination analysis. Recombination has been shown to significantly contribute to luteovirus diversity. To examine whether recombination events have occurred in the CABYV population, we aligned full-length nt sequences of 33 CABYV and 2 MABYV isolates using the Geneious method in Geneious Pro 8 and analyzed them using the RDP, GENECONV, BootScan, MaxChi, Chimaera, SiScan and 3Seq methods implemented in the RDP4 software with a highest acceptable P-value of 0. 01. In total, 56 potential recombinant events were detected by at least one method; however, to reduce error, we included only recombination events supported by at least three methods. Using this criterion, 24 recombination events, including 12 recombination types, were detected in 17 CABYV isolates (Table  6 ). Among the Korean CABYV isolates, nine recombination events were detected in eight isolates of types 5, 7, 8, and 9.
However, using 3' proximal proteins and the 3' UTR, the Korean CABYV isolates were classified into two subgroups within the Asian group. This shows the possibility of recombination in the IR region between the 5' proximal and 3' proximal proteins. Although the CABYV-R group belonged to CABYV, it grouped into MABYV in the 5' proximal protein and 3' UTR-based phylogenetic analyses, due to recombination between CABYV and MABYV isolates  (Knierim et al. , 2013). To confirm the results of sequence and phylogenetic analyses, we investigated whether recombination occurred in the CABYV population using the RDP4 software. Twenty-four recombination events of 12 recombination types were detected in the analyzed CABYV population. Among them, nine recombination events occurred in the Korean CABYV isolates.
High-throughput sequencing (HTS) has the potential to decipher the diversity of T cell repertoires and their dynamics during immune responses. Applied to T cell subsets such as T effector and T regulatory cells, it should help identify novel biomarkers of diseases. However, given the extreme diversity of TCR repertoires, understanding how the sequencing conditions, including cell numbers, biological and technical sampling and sequencing depth, impact the experimental outcome is critical to proper use of these data. Here, we assessed the representativeness and robustness of TCR repertoire diversity assessment according to experimental conditions. By comparative analyses of experimental datasets and computer simulations, we found that (i) for small samples, the number of clonotypes recovered is often higher than the number of cells per sample, even after removing the singletons; (ii) high-sequencing depth for small samples alters the clonotype distributions, which can be corrected by filtering the datasets using Shannon entropy as a threshold; and (iii) a single sequencing run at high depth does not ensure a good coverage of the clonotype richness in highly polyclonal populations, which can be better covered using multiple sequencing.
Tr library Preparation RNA was extracted following the manufacturer's recommendations and cDNA synthesis was performed with the Qiagen OneStep RT-PCR kit (Qiagen Inc. , Valencia, CA, USA) and mouse T cell beta receptor primers provided with the mouse TRB iR-Profile Kit (iRepertoire Inc. , Huntsville, AL, USA). cDNA was amplified by two rounds of PCR according to the manufacturer's recommendations. The TRB library was sequenced using Illumina on a MiSeqv2 kit. repseq Data Processing.
Diversity Profiles Rényi entropy is a generalization of Shannon entropy, initially developed for information theory. We applied this mathematical function to clonotype frequencies to assess their diversity within each dataset. Rényi entropy is function of a parameter α, a strictly positive real number that differs from 1 and allows the definition of a family of diversity metrics spanning from (i) the species richness (α = 0), which corresponds to the number of clonotypes regardless of their abundance, to (ii) the clonal dominance (α → + ∞), corresponding to the frequency of the most predominant clonotype. For α = 1, the Shannon diversity index is computed. The exponential of the Rényi entropy defines a generalized class of diversity indices called Hill diversities, which can be interpreted as the effective number of clonotypes in the datasets  (49)  and thereby is used to build a diversity profile.
repseq simulation algorithm A. 2•10 6 clonotype library construction with the tcR package Based on the estimated total number of clonotypes in a mouse, a 2•10 6 TRB CDR3 library was generated with the tcR package following the probability rules of V(D)J rearrangement established in  Murugan et al. (50) : Ω = = ⋅ ∀ with 2 1 is a clonotype genera   41 ) that clonotype frequencies determined from RepSeq datasets generally follow a Zipf distribution with a particular α ∈ [0, 1] parameter, we chose to use the Zipf-Mandelbrot law implemented in the zipfR R package  (51)  to simulate clonotype distributions. The probability density function used for simulations is given by For each Zipf parameter combination, a list ZA is randomly generated as follows:  C. For each A parameter, the 2•10 6 ZA values were randomly assigned to the clonotype collection to obtain seven TRB clonotype repertoires.
D. To obtain the final seven datasets, each of them was rarefied using the function rrarefy from Vegan R package to datasets of with a size of 6•10 5 sequences. g C B ( ): π π π α = ⋅ ≤ ≤      --1 Z z z z i z i A A A A A i = ∀ ∈ ∀ with 1 2 NA { ; ;. }, , , , , , ,  + * j j i j z z N A i A j A , , , if then 2 1 6 ≤ ≥ = ⋅ 0 ZA elements follow a Zipf distribution of A (= 1/α) parameter.
Rarefaction at Increasing Sizes Each of the seven simulated datasets was rarefied into a series of six datasets of size D ∈ {500, 1,000, 5,000, 5•10 4 , 5•10 5 , 1•10 6 }. For each value of D, subsamples of TRB sequences were randomly produced using the vegan:rrarefy function (without replacement). This process was iteratively repeated 100 times with replacement. For each resulting series of subsamples, clonotype counts were calculated and used to assess the median and 95% CI values of Morisita-Horn index [MH;  (52) ] between them and the original dataset (representativeness) and between each other (robustness). Subsample compositions were also compared to evaluate the level of overlap between three subsamples according to the dataset size. For each D, combinations of 3 ZA dataset subsamples were randomly selected to determine the proportion of clonotypes observed once, twice or in the three subsamples.
The point here is to determine whether the sample size will impact the resulting repertoire distribution. Thus, we analyzed the diversity of the observed repertoires according to sample size. It is noteworthy that the number of unique clonotypes (i. e. , unique combination of TRBV-CDR3pep-TRBJ) per sample was always higher than the number of cells per sample. This discrepancy was more marked for small size samples, with approximately 20-to 2-fold more clonotypes per sample than cells with the "500-" and "50,000-cell" samples, respectively. In each dataset, about 50% (±6%) of the clonotypes were observed once (singletons). After removing the singletons, as it is commonly done  (44) , this bias was reduced for the large samples, while the numbers of clonotypes remained much higher than the actual number of cells in small samples (Figure  1B ).
Still, overall richness remained equivalent between all sample sizes. In order to refine the diversity assessment of these TRB repertoires, we computed their diversity profile (Figure  1C ) applying Rényi entropy to the clonotype relative frequencies within each dataset. This function is used in ecological science to quantify the diversity, uncertainty, and randomness of a given system  (54, 55). As the α order increases, it defines metrics spanning from (i) the species richness to (ii) the clonal dominance that progressively discards the scarcest species. The exponential of these metrics provides comparable effective numbers of species, used here to build a diversity profile. Analysis of the Rényi profiles for the eight aliquots showed that TRB repertoire diversity strongly decreases when the Rényi order α value increases.
shannon entropy as a Threshold to Filter the clonotypes To avoid bias related to sample size, we normalized each dataset to 700,000 sequences, a value corresponding to the smallest sample size (Table  S1  in Supplementary Material). Therefore, we randomly selected 700,000 sequences, ranked the unique clonotypes from the most to the least predominant (clonotype rank) and plotted their abundance (clonotype count) to assess their distribution (Figure  2A ). It is noteworthy that, while all the aliquots come from the same sample, the clonotype distributions within each dataset are different. The smaller a sample, the higher the most predominant clonotype counts, making it difficult to apply a filtering rule based on the count values. The Rényi profiles (Figure  1C ) showed that the repertoire diversity collapses at a Rényi order α of 1, which corresponds to the Shannon diversity index  (56).
Since the number of clonotypes assessed by the Shannon index (Table  1 ) correlates best with sample size (Pearson coeff = 0. 966, p = 9. 62•10 -5 and MH = 0. 877 on original clonotype number and Pearson coeff = 0. 995, p = 2. 92•10 -7 and MH = 0. 996 after clonotype number determined by Shannon index), we chose to use this metric as a threshold to discard scarce "uninformative" clonotypes (SUC) that could result from experimental noise (shown in gray in Figure  2A ) and keep only "informative" ones. As shown in Figure  2B , the clonotype relative distribution within each dataset is not significantly altered by this filtering. Interestingly, as shown in Figure  2C , regardless of the initial number of cells, this transformation regularizes the values of the Piélou evenness index, a measure of clonotype evenness (57) (filled squares), which otherwise strongly decreases for unfiltered datasets when the clonotype number/cell number ratio increases, revealing that a too high sequencing depth for small samples alters clonotype distributions (Figure  2C , empty circles).
To confirm that the filtering does not bias the overall repertoire diversity, we computed the Morisita-Horn (MH) similarity index between the datasets before and after filtering; the high similarity values (0. 983; 0. 997) shown on the matrix diagonal in Figure  2D  confirm that the datasets are not altered in the process. The similarity matrix also reveals a low similarity between replicates, except for the "50,000-cell" samples, which are big enough to share rare clonotypes. Thus, high sequencing depth does not ensure good coverage of clonotype richness. This led us to question the robustness of RepSeq experiment results.
robustness of the TrB repertoire Diversity assessment by repseq We sorted 3•10 6 Teff cells from splenocytes, extracted the RNA and split it into three equivalent RNA aliquots, and then sequenced them independently at a high-depth targeting the TRB chain using the iRepertoire ® multiplex-PCR technology. On average, for each aliquot, 8. 33 (±0. 66) million reads were produced and 5. 63 (±0. 56) million TRB sequences were identified, among which an average of 130•10 3 (±5•10 3 ) clonotypes (Table  S2  in Supplementary Material). After applying Shannon filtering, the dataset sizes were reduced to 4. 7 (±0. 6) million TRB sequences for a total of 44,217 (±304) clonotypes. Datasets were rarefied at an equivalent size by randomly selecting 4•10 6 sequences for each sample. We first analyzed the clonotype distributions within each dataset.
The three distributions were similar between replicates (Figure  3A ). However, when we compared the composition of the three TRB repertoires by clonotype overlap, it appeared that about 36% of the clonotypes observed in each dataset are shared by another replicate, with only 6,599 clonotypes common to the three replicates. Although these shared clonotypes represent only 6% of the 105,332 clonotypes identified overall, their expression accounted for approximately 38% of each repertoire (Figure  3B ). Within each dataset, clonotypes were ranked according to their counts from the most to the least predominant (decreasing clonotype rank) and their abundance (clonotype count). Both axes are log-scaled. Plots were colored according to the sampling size: "500 cells" in red, "1,000 cells" in green, "5,000 cells" in cyan, and "50,000 cells" in purple.
Clonotypes filtered out using the Shannon index (see main text) are colored in gray [scarce uninformative clonotypes (SUC)]. (B) TRB clonotype distributions of the eight aliquots before and after data filtering. Before (left) and after (right) filtering each dataset using the Shannon index as threshold, clonotypes were ranked from the most to the least predominant (decreasing clonotype rank) according to their relative frequencies (clonotype frequency). The X-axis is log-scaled. Distributions were colored according to the sampling size as previously. (c) Impact of clonotype filtering on the clonotype distribution evenness. The ratio between the number of clonotypes and the number of cells (x-axis) was calculated for each aliquot before (circles) and after clonotype filtering either by removing only singletons (triangles) or using the Shannon index as a threshold (squares).