dataesr/scientific-paragraphs-categorization huggingface dataset:
SCG-M, MBG, JA and FB interpreted the results. JA led the writing of the manuscript. All authors contributed to the manuscript and gave nal approval for publication. 8 Data and script availability Data are publicly available. SNP data were deposited in the Dryad repository at http://dx. doi. org/10. 5061/dryad. 8d6k1. Height data have been deposited in GENFORED, the Spanish Network of Genetic Trials (http://www. genfored. es). Scripts are available at https://github. com/ JulietteArchambeau/HeightPinpinClonapin.
-sample explanatory power, using training datasets) and predictive ability (out-of-sample predictive power, using test datasets). In P1, we aimed to predict new observations, an observation being a height-growth measurement in a given year on one individual. P1 corresponds to a random split of the data between 75% of observations used to t the models (the training dataset of 24,840 observations) and 25% of observations used to evaluate model predictions (the test dataset of 8,281 observations). Notice that the test dataset of the P1 partition was not totally independent from the training dataset as it belongs to the same genotypes/provenances and blocks/sites. In P2 and P3, we aimed to predict new provenances. P2 corresponds to a random split between a training dataset of 28 provenances and a test dataset containing the remaining 6 provenances.
P3 corresponds to a non-random split between a training dataset of 28 provenances and a test dataset containing 6 provenances with at least one provenance from each under-represented gene pool (i. e. northern Africa, south-eastern Spain and Corsican gene pools; see section 6. 3 of the Supplementary Information for details). erefore, the test datasets of the P2 and P3 partitions represent fully independent sets of provenances. To evaluate the model goodness-of-t, we calculated the in-sample (in the training dataset) proportion of the variance explained by each model m in each common garden s, conditional on the age e ect, such as: R 2 ms |age = (V pred ms -V age 2s )/(V y s -V age 2s ), where V pred ms is the variance of the modeled predictive means from model m in site s, V y s the phenotypic variance in the site s and V age 2s the variance explained by the age e ect in the model M2 in site s.
Acknowledgements We thank  A. Salda  ña, F. del Ca ño,  E. Ballesteros  and  D. Barba  ( INIA ) and the  'Unité Expérimentale Forêt Pierroton' (UEFP, INRAE ; doi: 10. 15454/1. 5483264699193726E12 )for eld assistance (plantation and measurements). Data used in this research are part of the  Spanish Network of Genetic Trials  (GENFORED, http://www. genfored. es). We thank all persons and institutions linked to the establishment and maintenance of eld trials used in this study. We are also very grateful to  Ricardo Alía  who contributed to the design and establishment of the CLONAPIN network and provided comments on the manuscript, and to  Benjamin Brachi , ibault Poiret,  Andrew J. Eckert  and one anonymous reviewer who provided constructive and valuable comments on a previous version of the manuscript.
anks are extended to  Juan Majada  for initiating and supervising the establishment of the CLONAPIN network. JA was funded by the  University of Bordeaux  (ministerial grant). is study was funded by the  Spanish Ministry of Economy and Competitiveness  through projects  RTA2010-00120-C02-02  (CLONAPIN),  CGL2011-30182-C02-01  (AdapCon) and  AGL2012-40151-C03-02  (FENOPIN). e study was also supported by the  'Initiative d'Excellence.
2. 5 Comparing model goodness-of-t and predictive abilityree partitions of the data (P1, P2 and P3) were used to evaluate model goodness-of-t (i. e. inFinally, to evaluate the model predictive ability, we calculated the out-of-sample (in the test dataset) proportion of the variance predicted by each model m in each common garden s conditional on the age e ect, that we called prediction R 2 ms |age. Details about calculating prediction R 2 ms |age and some supplementary indexes used for model comparison are presented in section 5 of the Supplementary Information.
dei. uc. pt/star. This repository is available to the community for reporting improving results and it will be continuously updated for different values of m and d. The BB code for d = 2 is available at https://github. com/luis-paquete/StarDSS. Structure of the Paper. We recall in Section 2 relevant definitions and properties of the L ∞ star discrepancy problem. In particular, we summarize known bounds, address computational aspects of evaluating the L ∞ star discrepancy of a given point set, and briefly introduce the point sets that we consider in the experimental analysis. In Section 3 we introduce the discrepancy subset selection problem, prove NP-hardness of its associated decision problem, and discuss some basic properties that are explored by MILP. Our subset selection algorithms are presented in Section 4, while a comparison in terms of running times and solution quality is provided in Section 5.
Experimental setup The Sobol, Halton and RevHal point sets were generated by a program written in C using GNU Scientific Library, namely, library gsl_qrng for the generation of quasi-random sequences, with the procedures gsl_qrng_sobol, gsl_qrng_halton, and gsl_qrng_reversehalton, respectively. The sequences unif were also generated in a similar way, using library gsl_rng for random number generation with the procedure gsl_rng_uniform. Faure and iLHS point sets were generated in R using procedure runif. faure available in the DiceDesign package and procedure improvedLHS available in the lhs package, respectively. Fibon sets were generated by a code in Python (version 2. 7. 16) written by the authors. For the two-dimensional case, we considered m ∈ {20, 40, 60, 80, 100, 120} and for each value of m, we considered n ∈ {m + 20, m + 40,.
1. The MILP formulation was written in an LP format, which is read and solved by SCIP solver with the default parameters. The BB algorithm for two and three dimensions and with the incremental computation of lower bounds as described in Section 4. 2 was written in C. In a preliminary step, the points were sorted in increasing order with respect to the first dimension to prepare them for the application of our solvers. To run the experiments, we used a computer cluster Dell PowerEdge R740 Server with two Intel Xeon Silver 4210R 2. 4G, 10 Cores / 20 Threads, 9. 6GT/s, 13. 75M cache, with two 32GB RDIMM, two 480GB SSD SATA hard-drives, and Debian GNU/Linux 10 (buster) operating system. The running times in seconds of the SCIP solver and of the BB program were measured with command time under linux, with a cut-off time limit of 30 minutes.
Unfortunately, MILP is not feasible for the 3d case as the memory requirement grows very fast, reaching the limit available in the cluster for the smallest instances. This is due to the large number of constraints in the 3d case. In fact, a file with the MILP formulation in LP format occupies several gigabytes. For this reason, Tables 2 and 4 report only the CPU-time taken by BB on deterministic and randomized sequences, respectively. The performance decay of BB reported in these tables is noticeable in comparison with the 2d case. For instance, this approach cannot find a single solution for 3d instances with n = 100 and m = 20, whereas it can  solve all 2d instances for the same values of m and n in at most 250 seconds. This is mainly due to the time taken with the update of data structures and the evaluation of the lower bounds, which grows considerably with an increasing number of dimensions.
Acknowledgments We thank  Magnus Wahlström  for providing his implementation of the algorithm from [ DEM96 ] to evaluate the star discrepancy. We also thank  Michael Gnewuch  and  Aicke Hinrichs  for suggesting to add Fibonacci sequences to our experiments in 2d, and we thank  Gonçalo Martins  for performing preliminary experiments on branch and bound [Mar19]. Last but not least, we thank the reviewers for their constructive feedback and for pointing us to the work of Dwivedi et al. on online thinning [DFGGR19]. This work was funded by the  Paris Ile-de-France Region  and by national funds through the  FCT -Foundation for Science and Technology, I. P. within the scope of the project  CISUC -UID/CEC/00326/2020.
Raw and weighted patients' characteristics are presented in the Table  1. Hereafter, results presented always come from the weighted dataset. Raw results are available in the Supplementary Materials. In the weighted data, patients' median age was 48 years (interquartile range 32-56) with 57. 7% (559/968) men. In all, 35. 1% (340/968) reported comorbidities, 6. 3% (61/968) had chronic lung diseases, and 4. 2% (41/968) high blood pressure. The median time between symptom onset and the last follow-up was 174 days (IQR 97 to 284 days). Among participants, 75 (7. 7%) had been hospitalised during their acute disease and 34 (3. 5%) had been admitted to an intensive care unit (ICU). Probability of symptom persistence at 12 months. Participants were followed up every 60 days with online questionnaires available on computer or smartphone.
At each observation point, patients were first asked if they still had symptoms related to COVID-19. Those reporting the persistence of symptoms  completed the long COVID symptom tool (ST) and impact tool (IT), a pair of validated patient-reported instruments assessing respectively 53 symptoms and 6 dimensions of patients' lives that can be affected by the disease  18. Those reporting that they no longer had any symptoms were asked to report the date when they first noticed the absence of symptoms. Overall, our data covered 5350 person-months, with a median follow-up since cohort enrolment of 181 days (interquartile range 118 to 240 days). The proportion of patients lost to follow-up was <20% at every follow-up (Supplementary Material 1). Remission of symptoms (i. e. , disappearance of all symptoms) was observed during follow-up for 150 patients.
The recruitment of volunteers in the ComPaRe long COVID cohort may have selected patients who had overall more symptoms, and our recruitment strategy included a social media and general media campaign that may have selected younger and better educated patients. Second, our sample included a majority of women. Persistent symptoms after COVID-19 are more frequent among women; for example, the odds of persistent fatigue at 1 year among women compared with men was 1. 43 [1. 04-1. 96]  26. To minimise these issues, we used a weighted dataset with weights derived from the September 2021 data from the Office of National Statistics in the United Kingdom  16. Yet, because our original dataset included fewer men, results in this subgroup may lack precision. More generally, the subgroup analyses were post-hoc, and should be considered as exploratory.
Participants interested in participating in ComPaRe can enrol themselves on the cohort's online website (https://compare. aphp. fr). All patients provide electronic consent before participating in the e-cohort. After completing initial demographic and clinical information, patients receive invitations by e-mail to answer online questionnaires related to their diseases and/or treatment, as well as prompts to participate in research projects nested in ComPaRe. Participants. In this study, we analysed the data from patients (1) reporting a laboratory-confirmed COVID-19 infection, with a positive test result for SARS-CoV2 by PCR swab and/or a serological assay; (2) reporting at least one symptom persisting 2 months after symptom onset, in a validated list of 53 symptoms;  18  and (3) who enroled in the cohort by August 1, 2021, so that they would have at least 2 months of follow-up.
Those reporting that they no longer had any symptoms were asked to report the date when they first noticed the absence of symptoms. The vital status of ComPaRe participants is assessed regularly by linkage with the French National Death Register (INSEE, fichiers des personnes décédées). As of October 28, 2021, no patient enroled in the cohort had died. Analyses. Because we used data from an e-cohort, we had no missing data within validated online questionnaires. To enhance the representativeness of our estimates, we used a weighted dataset obtained by calibration on margins with weights for age (<24, 25-34, 35-49, 50-69, ≥70 years old), gender and hospitalisation during the acute phase of the disease derived from the data from the Office of National Statistics in the United Kingdom  16, 17.
A data-driven research approach may unlock the full potential of teff agrobiodiversity and propel breeding for new improved varieties matching local needs. Here, we report a transdisciplinary data-driven approach to characterize NUS genetic, agronomic, and climatic diversity, using teff as a case study. We selected and genotyped 321 teff farmer varieties derived from landraces and 45 teff improved lines with high-density molecular markers, and we characterized their agronomic performance at two locations in Ethiopia. Experienced teff farmers (15 women and 20 men) were asked to evaluate the teff genotypes, providing qualitative information that could allow us to prioritize better adapted genetic materials. We derived current and projected climate data at the sampling locations of teff accessions and used them to estimate genetic offset under climate change scenarios.
ddRAD libraries were sequenced with V4 chemistry on Illumina HiSeq2500 sequencer (Illumina, San Diego, CA) with 125 cycles in a paired-end mode. Reads were demultiplexed using the process_radtags utility included in Stacks v2. 0  (Catchen et al. , 2013)  and analyzed for quality control with the FastQC tool (v. 0. 11. 5). Highquality paired-end reads of each individual were mapped against the Eragrostis tef reference genome (version 3, available from CoGe under ID 50954)  (VanBuren et al. , 2020)  with BWA (Burrows-Weeler-Aligner v. 0. 7. 12) using the MEM algorithm with standard parameters  (Li, 2013). Alignments were sorted and indexed with PicardTools (http://broadinstitute. github. io/picard/) and samtools  (Li et al. , 2009). Single nucleotide variants were identified with  GATK (McKenna et al.
Bioclimatic characterization GPS coordinates of EtDP teff landraces were derived from EBI passport data and projected onto the map of Ethiopia using R/raster  (Hijmans and van Etten, 2012). Teff landraces were projected onto the agroecological-zones map of Ethiopia provided by the Ethiopian Institute of Agricultural Research (EIAR)  (MoA, 2000) , which subdivides Ethiopia into different zones according to altitudinal ranges and temperature and rainfall patterns. Altitudes were assigned to each landrace based on GPS coordinates, using the CGIAR SRTM database at 90 m resolution  (Reuter et al. , 2007). Current climate data  (1970-2000 averages)  relative to teff landraces' sampling sites were retrieved from the WorldClim 2 database of global interpolated climate data  (Fick and Hijmans, 2017)  at the highest available spatial resolution of 2.
2 m inter-row distance. Three phenological traits, days to 50% heading (DH), days to 90% maturity (DM), and grain filling period (GFP) were recorded on whole plots in each environment. Morphology and agronomic traits were recorded from five randomly selected teff plants per plot: plant height (PH, cm), panicle length (PL, in cm), number of primary branches per main shoot panicle (PBPM), number of total tillers (TT), first culm length (CLF, in cm), first culm internode diameter (CDF, in mm), panicle weight (PW, in grams), panicle yield (PY, in grams), grain yield (GY, ton/ha), biomass yield (BY, ton/ha), harvest index (HI), grain yield filling rate (GFR, kg/ha/day), and biomass production rate (BPR, kg/ha/days). Qualitative data was sourced from the characterization performed by Woldeyohannes and collaborators  (Woldeyohannes et al.
Genetic diversity Phylogenetic relationships in the EtDP were assessed on a pruned set of SNP markers with MAF > 0. 05. Pruning was performed with the PLINK  (Purcell et al. , 2007)  indep-pairwise function on a 100 SNPs window moving in 10 SNP steps with a linkage disequilibrium (LD) r 2 threshold of 0. 3. Pairwise identity by descent (IBS) was calculated by PLINK and visualized with custom scripts in R (R  Core Team, 2018). A principal component analysis (PCA) and a discriminant analysis of principal components (DAPC) were performed with R/adegenet  (Jombart, 2008). The optimal number of clusters (K) for the DAPC was identified using adegenet::find. cluster(). Bayesian Information Criterion (BIC) statistics were computed at increasing values of K to measure the goodness of fit at each K using 365 PCs and default settings.
Climate projections for areas of interest were analyzed to assess trends in rainfall and temperature. The twelve models best performing in the East Africa region according to IPCC (IPCC, 2017) were used to develop and ensemble projection of rainfall and temperature indices with Climate Data Operators (CDO)  (Schulzweida, 2017)  and custom R scripts. Projected data was compared with historical data to derive indices change in the interannual variability for the regions of interest.
Acknowledgments We wish to thank the men and women farmers who took part to the research, evaluating hundreds of genetic materials with great enthusiasm and competence in Adet and  Akaki. These  are their names in alphabetical order: Abebech,  Adisse Abera ,  Adugawu Kere, Alebachew, Angoachi Fente, Askale, Ayana, Belachew Adimasu, Belay, Birtukan Ayele, Birhanu, Genanaw Dejene, Getasil ,  Habtamu Belay ,  Mame Seyum ,  Mamo ,  Misganaw Dagnaw ,  Regassa ,  Sefiwu Kassahun ,  Shashe ,  Tenagne Wubet ,  Tesfa Kefyalew ,  Tihune Dires ,  Tirusew ,  Tsehay Desie ,  Workinesh ,  Workineh Tsega ,  Wubet, Wubitu ,  Yechale Ayalneh ,  Yekoye ,  Yeniguse Worku ,  Yeniguse Wuletaw ,  Zebu Kebede , and  Zina Yitay. We thank the  Ethiopian Biodiversity Institute (EBI)  for providing seed material and laboratory space to perform the DNA extraction in Ethiopia.
Data availability Teff accessions are available upon request from the Ethiopian Biodiversity Institute (EBI, http://www. ebi. gov. et/). Raw DNA sequencing reads are available on the Short Read Archive (https://www. ncbi. nlm. nih. gov/sra/) under BioProject accession number PRJNA758057. All scripts used for data analysis are available on GitHub at https://github. com/mdellh2o/TeffDiversityPanel.
1, 15 and 16, respectively). To determine whether a mutation was fixed or lost, we drew a number in U 0; 1 ð Þ, and if this number was smaller than P f the mutation was fixed, otherwise it was lost. If the mutation was fixed, the mutant became the new resident, i. e. all individuals displayed the same genotype and phenotype. If the mutation was lost, genotype and phenotype were unchanged. We then proceeded to the next time step. Thus, a time step is the interval between two successive mutations. Populations evolved over 125,000 time steps, and the system values were recorded every 250 steps. These simulations allowed us to relax some of the assumptions of the mathematical analysis, namely small mutation effects, large population size and the absence of fixation of deleterious mutations. Data and code availability.
The experimental data challenge Our theoretical predictions have now to be compared with experimental data. The extensive databases of quantitative proteomics (ProteomeXchange  (Deutsch et al. , 2020) , PRIDE  (Perez-Riverol et al. , 2019) , etc. ) and metabolic pathways (KEGG PATH-WAY  (Kanehisa et al. , 2021) , BRENDA  (Chang et al. , 2021) , SABIO-RK  (Wittig et al. , 2012) , etc. ) can be sources of relevant information to address the questions we raised. As an example, we present in Supporting Information SII the results of an analysis we performed from data of the MetaColi project  (Sabarly et al. , 2016). We retrieved enzyme abundances and activities of 16 enzymes of the central carbon metabolism measured in five Escherichia coli strains grown in four culture media. Even though these data are not formally the same as ours (the units are different, the Michaelis-Men-ten constants are not explicit and the data come from different pathways), they allowed us to show that there are frequent negative correlations between enzyme activities and abundances, whatever the culture medium (Fig.
Acknowledgements We warmly thank  Dr. Wolfram Liebermeister  and  Dr. Arnaud Le Rouzic  for their careful reading of the manuscript and their very helpful suggestions. We also thank  Pr. Amandine Veber  for her useful suggestions regarding the differential equation system of enzyme evolution and  Hélène Citerne  for language corrections of the manuscript. Funding: CC was supported by a PhD thesis grant from the  French Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation.
The results were normalized to mouse Gapdh or human GAPDH expression using the Q-gene program (Equation (  2 ))  [17]. RNAsequencing libraries were produced with NEBNext Ultra II Directional RNA Library Prep Kit (New England Biolabs, Ipswich, MA, USA) (PSEN1 ∆E9 n = 5, CTRL n = 6). Libraries were sequenced with NextSeq500 (Illumina, 1 × 75bp, high output) using the services of the Biomedicum Functional Genomics Unit at the Helsinki Institute of Life Science and Biocenter Finland, University of Helsinki. Sequence reads were aligned simultaneously to the human genome (GRCh. 38) and mouse genome (GRCm. 38) using STAR aligner v2. 7. 8a  [18]  and annotated to gene exons with HTSeq v0. 11. 2  [19]  (RRID:SCR_005514). Nucleotide variation between the species allowed us to distinguish between sequence reads of the mouse or human origin.
On average, 78. 34% of total reads had a unique best alignment on either genome or were usable for HTSeq annotation. Differential gene expression (DEG) analysis of PSEN1 ∆E9 cell-transplanted versus CTRL-transplanted hippocampi was performed using the R package DESeq2 v1. 32. 0 method  [20]  (RRID:SCR_015687). DEG data that met the p-value < 0. 05 and fold change ≥ 1. 2 (in both directions) cutoff were further analyzed using QIAGEN Ingenuity Pathway Analysis (IPA)  [21]. IPA identified the nervous system-specific pathways from the QIAGEN IPA library that were most significant to the dataset by first calculating a ratio of the number of molecules from the dataset that map to a pathway divided by the total number of molecules that map to the same pathway. Second, a right-tailed Fisher's Exact Test was used to calculate a p-value determining the probability that the association between the genes in the dataset and the pathway is explained by chance alone.
Z-score ≤ -1. 99 or ≥ 1. 99 was used as a cutoff for a directional change. Cell type enrichment analysis was conducted by comparing DEGs from our dataset (p < 0. 05) to the CellMarker database  [22]  augmented with co-expression RNA-seq data from ARCHS4  [23]  using Enrichr online tool  [24]  (RRID:SCR_001575). The heatmap of oligodendrocyte-associated genes was created using the Heatmapper online tool (RRID:SCR_016974; www. heatmapper. ca; accessed on 12 December 2022)  [25]. Freshly frozen cortices (PSEN1 ∆E9 male n = 6, female n =3, CTRL male n = 5, female n = 6) were homogenized in 10× volumes of TBS supplemented with Pierce protease inhibitor (Thermo Fisher Scientific) using a motorized plastic pestle and kept for 30 min on a rotator at 4 • C. The samples were centrifuged for 30 min at 16,000 × g at 4 • C, and supernatants were collected to assess the level of soluble Aβ species.
Statistics Statistical analyses were performed with GraphPad Prism 9. 2. 0 (Insight Partners, New York, NY, USA) or IBM SPSS Statistics software, version 27. 0 (IBM Corp. , Armonk, NY, USA) using Student's t-test, repeated measures ANOVA, or two-way ANOVA with Šidák'scorrected posthoc tests. Statistical significance was assumed at p < 0. 05. All data are shown as mean ± SEM. Significant outliers were detected and excluded from the analysis using the GraphPad outlier test.