dataesr/scientific-paragraphs-categorization huggingface dataset:
Purple bands on panels a, c, and d show 68% range of pteropod standing stock and daily/annual production calculated using the MAREDAT database (Methods). The blue stars on panel d show the estimates of total production calculated with in-situ pH and fCO 2 measurements at Ocean Station PAPA (ref. 57 , light blue), and estimates of production in the subpolar North Pacific calculated using the seasonal cycle of alkalinity and dissolved inorganic carbon (ref. 12 , dark blue). STG, TZ, and SPG represent subtropical gyre, transition zone, and subpolar gyre, respectively. productive subpolar gyre are higher (0. 9-1. 0 mol m -2 yr -1 ) than this global average even at the 95% confidence interval (Fig. 3d ); however, they agree well with the estimate of production calculated with in-situ pH and fCO 2 measurements at Ocean Station PAPA through (1.
13  in terms of the dominant CaCO 3 polymorph produced. We find CaCO 3 production is dominated by calcitic coccolithophores, however, their results suggested pelagic CaCO 3 production is mainly driven by aragonite pteropods with coccolithophores and foraminifera playing a minor role. Given the discrepancy with the results of Buitenhuis et al. 13  and the limited temporal interval of our sampling and the potential for large temporal variability of pteropod abundances  43  , we also calculate pteropod CaCO 3 biomass and production in the North Pacific using the comprehensive MAREDAT database  18  , which has excellent spatial and seasonal sampling distribution in the North Pacific (Methods, Fig. 4 ). This results in a typical pteropod CaCO 3 biomass in the upper 250 m of the North Pacific of 0.
5 mg m -3 (0. 2-1, 32-68% CI; note the dataset is highly skewed, Fig. S6 ), a vertically integrated pteropod CaCO 3 biomass of 122 mg m -2 (50-269, 32-68% CI), and a pteropod CaCO 3 production rate of 12 mg m -2 day -1 (5-27, 32-68% CI). The results calculated using the MAREDAT database are thus in good agreement with the estimates calculated using the samples collected during our own cruise (Fig. 3 ). We propose the discrepancy with the results of Buitenhuis et al. 13  instead comes from three other factors: Firstly, their model parametrization uses a fixed PIC/POC ratio of 0. 1 ref. 56  for coccolithophores; this value is substantially lower than the published review by Gafar et al. 25  which ranged from 0. 19 to 2. 30 and much lower than the value 0. 52 they used for pteropods, which is itself about two times higher than the estimates of Bednaršek et al.
Pteropod biomass and production estimated from MAREDAT Given the discrepancy observed with previous estimates of pteropod CaCO 3 production  13  and the sparse spatial and temporal resolution of our net-tow sampling, which unlike the coccolithophore data, cannot be independently verified using satellite data, we also estimate pteropod CaCO 3 biomass and production in the North Pacific using the MAREDAT database  17. The database has excellent spatial and temporal coverage of pteropod sampling within the North Pacific (Fig. 5 ). Similar to Bednaršek et al. 18  , we find no significant trends in biomass by latitude or time of year, so we perform our analysis using all samples spanning the entire North Pacific basin and for all months. We take all estimates of pteropod carbon biomass (reported in the database mg C m -3 ) from the upper 250 m of the water column, which we consider as the production layer, with the vast majority of pteropod biomass found within the upper 200m  18, 95.
This results in 1793 discrete observations. We include zero values (120 samples, <7% total) and remove 3σ outliers (16 samples, <1% total). Including these outliers results in unstable Gaussian Kernal densities (see below). We calculate the CaCO 3 biomass as the carbon biomass multiplied by the fraction PIC (fPIC), using PIC:POC estimates given in Table  1. We then integrate the standing stock over the production layer, and calculate the production by diving by the turnover time (Table  1 ) in the same manner as for the C-Disk-IV samples. We calculate the uncertainties via Bootstrapping of the dataset, propagating the uncertainty in PIC:POC and turnover time using Monte-Carlo simulation, using a highly-conservative flat probability distribution for both. Using a different depth range for the production layer (i.
e. 0-1000 m) has a negligible effect on our results. We estimate the probability of pteropod carbon and CaCO 3 biomass in the upper 250 m, integrated CaCO 3 biomass, and CaCO 3 production rate using truncated kernal density:  96  this approach truncates the densities below zero, up-weighting the values that are closest to zero, and thus deals better with the highly-skewed dataset containing zero values. In the North Pacific we find a typical CaCO 3 biomass in upper 250 m of 0. 5 mg m -3 (0. 2-1, 32-68% range), vertically integrated CaCO 3 biomass in upper 250 m of 122 mg m -2 (50-269, 32-68% range), daily CaCO 3 production of 12 mg m -2 day -1 (5-27, 32-68% range). Expanding our analyzes the global dataset of ref. 18  (i. e. not only the North Pacific subset described above) we find the global dataset is heavily skewed (Skewness = 13.
3; a value above 1 is considered Skewed), such that the mean value reported by ref. 18  is not a useful statistic to describe the dataset. Using the same kernal density method used above for the North Pacific subset we find a typical pteropod CaCO 3 biomass of 0. 02 mg m -3 globally using data from the upper 250 m, or 0. 04 mg m -3 , considering all data in the upper 1000 m (Fig. S6 ). These values are three orders of magnitude smaller than the mean pteropod CaCO 3 biomass value reported by ref. 18.
Acknowledgements We thank the captain and crew of cruise KM1712 on  R/V Kilo Moana. -Funding was provided by  NSF  Grants  OCE1220600  and  OCE1220302  awarded to JA and WB, respectively, MINECO  PID2020-113526RB-I00 , the  Generalitat de Catalunya MERS  (# 2017 SGR-1588 ) awarded to PZ and  NERC  grant  NE/N011716/1  awarded to JR. This work is contributing to the  ICTA-UAB "Unit of Excellence" (MINECO  ( CEX2019-000940-M) ). We thank  Heidi Block ,  Alasdair Murphy ,  Rory Abernathy ,  Joshua Cook ,  Jurema Domingos  and  Laura Simon  for assistance generating the foraminiferal standing stock data. We acknowledge the  Hawaii Ocean Time-series and Ocean Station PAPA  programs for data collected at their respective time-series stations.
12 26 4 C 3 )Fig. 4 | 434 Fig. 4| Pteropod CaCO 3 biomass estimates. a Pteropod CaCO 3 biomass estimated from the MAREDAT database 17  and measured in this study; note, maximum values extend above 2 mg m -3. Probability density of pteropod (b) Carbon biomass (c) CaCO 3 biomass (d) Integrated CaCO 3 standing stock (e) daily CaCO 3 production calculated using samples in the upper 250 m of the North Pacific from the comprehensive MAREDAT database 17, 18  (Methods). Red shading indicates 32-68% confidence interval range. Red values show the 32nd, 50th, 68th percentiles; orange value shows the value with the highest probability (all values given in mg). Note the distributions are highly skewed.
3. In Sec. 4, we apply a standard feature-mapping interface smoothing. Thereafter, we state both a classical isotropic and an adapted anisotropic material formulation in Sec. 5. For the combination of multiple spaghetti, we restate in Sec. 6 classical combination techniques for the isotropic material formulation and propose a novel approach for the combination of oriented anisotropic material. The proposed geometry is described by continuous, mesh-independent functions until here and we derive the associated mapping to a fixed grid in Sec. 7. Finally we present the compliance problem formulation in Sec. 8 and a numerical benchmark example in Sec. 9. The full source code of the presented approach is publicly available as part of the academic finite element simulation and optimization package openCFS.
Related Work Our model is close to the one of epistemic social choice, in which voters have noisy estimates of the candidates' true values. Some works investigate the quality of the results of specific voting rules in this model  [24, 8, 10]. Decisions based on the aggregation of scores appear in many contexts, not necessarily related to the human activity of social choice. In regression analysis, the quality of prediction can be improved by aggregating the results of different algorithms. For example, the methods of boosting and bagging achieve some form of aggregation  [28]. When several estimations are available for the same problem, it is common for some of them to be correlated, for example when the same technique is used with different hyperparameters or different training datasets. Since these correlations can be harmful for aggregation, many of the main methods require the panel of algorithms to be diversified  [14].
1 We call the resulting method Embedded Voting (EV). As an illustration, let us apply this method to the ideal case, in the limit of a large dataset (i. e. large m). By definition in Equation 4, all vectors ˆ e i have norm 1. When two agents are fully correlated, they have the same embedding vector. And when two agents i and i are independent, then by definition of independence, their vectors ˆ e i and ˆ e i are orthogonal in the limit of a large dataset. So the estimated embedding ( ˆ e i ) i will be the same as ( e i ) i , up to an orthonormal change of basis. It is then easy to prove that the singular values of Ŝ tend to (|A 1 |,. , |A k |, 0,. , 0) in the limit of a large dataset. The average singular value is ( l |A l |)/n = 1, so the number k of singular values higher than 0. 95 the average is precisely k.
Validation We now evaluate the performance of the aggregation rules that we have described. We use artificial datasets to have a full control over the behavior of the candidates and the agents. Broadly speaking, one performance measure goes as follows: • Draw for m candidates their utilities u(c j ), 1 ≤ j ≤ m. • Use a noise model to draw the agent estimations s i (c j )) 1≤i≤n,1≤j≤m. • Feed different aggregation rules with the estimates (the rules never access the utility values). • Measure with the utility values the quality of the returned candidate. • Repeat the steps above enough times to have a good measurement of the expected performance. In details, we draw the utility values of the candidates independently according to a normal distribution, which is chosen to be N (0, 1) without loss of generality.
Equivalently, let S(c j ) be the n × 1 random vector of the scores provided by the agents, Ē := σ d I n σ f E be the n × (n + k) matrix that concatenates the characteristics of distinct noise and feature noise (where I n denote the n × n identity matrix), and X j = (d 1,j ,. , d n,j , f 1,j ,. , f k,j ) T be a random vector whose n + k values follow i. i. d. standard normal distributions. Under our noise model, S(c j ) can be written as: S(c j ) = u(c j )1 n×1 + ĒX j. (6) Thus, conditionally on the value of u(c j ), S(c j ) follows a multivariate normal distribution of mean u(c j )1 n×1 and covariance matrix Σ := Ē ĒT. Let us illustrate the noise model on a few toy examples: in the degenerate case where E is a n × 0 matrix, i. e. Ē = I n , we have n 3 github. com/TheoDlmz/embedded_voting. independent agents; the case E = ⎛ ⎝ 1 0 1 0 0 1 ⎞ ⎠ with σ d = 0 models the example with two clones given in the introduction (more generally, the ideal case with k perfect groups introduced in Section 2.
Collected data Data were collected during working days in the gynecology, internal medicine, oto-rhino-laryngology, neurology, pulmonology, pediatrics, rheumatology, medical emergency, dermatology, hematology, infectious diseases and primary curative consultation department from selfadministration of a standardized in the HCWs present during the survey period. The questionnaire was pre-tested to ensure that it would be well understood by the respondents. In each participating department, the questionnaire was distributed to all present HCWs and was picked up the same day. The data were related to characteristics of the health facility including: type of facility (national hospital, Municipal hospitals, primary health centers), socio-demographic and professional characteristics including: gender, age, grade and professional experience (in years), information received on arbovirus diseases and sources of information.
Statistical analysis The categorical variables were summarized with percentages and the quantitative variables were described by median and interquartile range. Pearson's Chi-squared test or Fisher's exact test was used to determine significant differences between categorical variables according to their condition of use. To evaluate the relationships between these variables and the different outcomes, tree-structured models (Decision Tree and Inference Conditional Tree) were used. The best tree model was selected based on the high value of Accuracy and the low value of no information rate  [23, 24]. Before implementing the models, the oversampling technique was used to adjust for class balance. R Studio 3. 5. 3 software was used for data processing and analysis. Map created with QGIS software (source of administrative boudaries map layer: https:// www.
This study was funded by the  International Development Research Centre (IDRC). Funding was provided by the  IDRC  to the  Centre de recherche et de formation en infectiologie de Guinee (CERFIG)  as part of the  DOPERAUS project  ( Decentralize  and  operationalize the One Health platforms in Guinea and perceptions of HCWs regarding arboviruses, Decision Tree and Inference Conditional Tree  models were used. The project information can be found at the following addressdecentralize-andoperationalize-one-health-platforms-guinea-anddemocratic-republic-congo. None of the author received a grant received a grand with his/her name. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. None of the authors received a grant in their name.
Literature search and selection criteria The comprehensive data search was carried out with PUBMED (https://www. ncbi. nlm. nih. gov/pubmed/), Agricola (https:// agricola. nal. usda. gov/), CABI (https://www. cabi. org/publishingproducts/animal-science-database/). Scopus (https://www. scopus. com) and Google Scholar (https://scholar. google. com/) databases. The keywords used to create datasets in each search engine included "chromium and cattle", "chromium and cow", "chromium and cows", "chromium and dairy cow", "chromium and dairy cattle", "chromium supplementation", "chromium supplementation and cattle", "chromium supplementation and cow", "chromium supplementation and cows", "chromium supplementation and dairy cow", "chromium supplementation and dairy cows", and "chromium supplementation and dairy cattle".
For Google scholar, we searched each keyword for up to 10 pages. Additionally, we searched the Journal of Dairy Science (https://www. journalof dairyscience. org/action/doSearch?text1=chromium&field1=Abstra ctTitleKeywordFilterField) with only a single keyword "chromium" by applying filters to article title, abstract and keywords. The data of each parameter [DMI, milk production, milk fat percent, milk protein percent, milk lactose percent, and solids-not-fat (SNF) percent] were extracted only if the response of interest was evaluated during Cr supplementation. The data were excluded if Cr was supplemented for less than a week. The data were also excluded if the recording was done after supplementation of Cr had stopped because in some studies milk production and milk composition parameters were also evaluated after cessation of Cr supplementation to evaluate the carryover effects of treatments.
Additionally, data on body weight (BW), experimental duration/duration of Cr supplementation, parity of the cows, days in milk (DIM), stage of parturition [before (BFP) or after (AFP) parturition], Cr complexes (amino acids, yeast, picolinate, or propionate) were extracted from studies. The studies without variance (SE or SD), mean or data given in figures without means provided were also rejected. The overview of the studies included or excluded in the meta-analysis is provided in a flow chart (Figure  1 ). There are totally 26 studies  (8,  included for the meta-analysis.
Statistical analysis The statistical analysis was conducted using the mean difference (MD) as the outcome of the measure. A random-effect model was fitted to the data. A random-effect model allowed the true effect to vary from study to study and include between-study variability (true heterogeneity) as well as sampling error  (37). The outcome variables were DMI, milk production, milk fat content, milk protein content, milk lactose content, and SNF content. The forest plots were developed to visualize the summary effects of the random effects meta-analytical model for each outcome variables using the R package dmetar (version 0. 0. 9000) (38). The amount of heterogeneity (τ 2 ) was estimated using the restricted maximumlikelihood estimator. Tau-squared is a measure of heterogeneity in meta-analysis.
The multilevel meta-analysis was carried out using the metafor R package (version 3. 0. 2)  (44). The variance distribution was evaluated by the R package dmetar (version 0. 0. 9000) (38). The τ 2 values of the models were compared with or without a moderator to evaluate the decrease in heterogeneity. Influence analysis for heterogeneity identification was carried out for the response of interest (DMI, milk production, fat, protein, lactose, and SNF) using the Baujat diagnostics. The Baujat et al. (45)  diagnostics identified the respective contribution of each study.
Publication bias A contour-enhanced funnel plot for each outcome was created to assess the risk of bias in the studies included in the meta-analysis. The standard error of the observed outcomes as predictor was used to check for funnel plot asymmetry. The symmetrical distribution of studies around the calculated MD indicated no risk of bias, while an asymmetrical distribution around MD was an indication of the potential risk of bias. The presence of bias was identified by Egger's test  (46) , and P < 0. 05 indicated the presence of bias in the funnel plot. The analysis was carried out using R package metafor (version 3. 0. 2)  (44).
Due to these missing covariates, we analyzed our data as individual moderators. In the multiple meta-regression model, the R package metafor  (44)  automatically drops missing variables completely from the analysis. Intuitively, we would expect that the estimate of τ 2 in the metaregression model must be lower (or at least, no larger) than the estimate of τ 2 from the random-effects model. However, we also observed an increase in τ 2 for the dose of Cr and experimental duration/supplementation duration. In classical regression, adding a predictor can only decrease the residual variance. But in a multilevel model, adding an individual predictor can sometimes make the group-level variance go up. This can happen when the individual-level predictor is negatively correlated with the group coefficient  (47).
Chromatinization and 3D folding of the 2µ plasmid The 2µ plasmid sequence is typically filtered (or overlooked) from Hi-C analysis and more generally from any high-throughput sequencing data. We therefore revisited ten years of datasets to explore this overlooked sequence. Using Micro-C, a high-resolution Hi-C derivative that quantifies DNA contacts at the nucleosomal level  (Hsieh et al. , 2016; Swygert et al. , 2019) , we generated a plasmid contact map at 200 bp resolution from cells in exponential growth (Fig. 1a ; Methods). The cis-contacts map revealed four small self-interacting regions corresponding to the four plasmid genes, reminiscent of the pattern observed at the level of active chromosomal genes  (Hsieh et al. , 2016). The ~1 kb STB-ORI region, positioned in between the RAF1 and REP2 genes, appeared as a constrained region with a stronger local enrichment in short range contacts.
Characterisation of hot spots of contact of the 2μ plasmid To further characterize the composition of the regions contacted by the 2μ plasmid, we computed the average enrichment of various genomic signals (ChIP-seq, MNAse-seq, ATAC-seq) over the 73 contact hotspots. For example, we computed the histone H3 ChIP-seq average signal and observed an enrichment at the hotspots (Fig. 3a ). In agreement with the transcriptional inactivity of the hotspots, chromatin accessibility (ATAC-seq)  (Lee et al. , 2018)  shows that these regions are less accessible compared to the rest of the host genome (Fig. 3b ). To determine the chromatin composition of the chromosomal regions contacted by the 2µ plasmid, we also took advantage of a recently generated ChIP-exo dataset to screen for the deposition of ~800 different proteins or histone marks along the S.
Discussion In this work, we exploit overlooked genomic datasets to confirm that the 2μ plasmid chromatization is highly very similar to its host, potentially contributing to its long co-existence within its host's nucleus  (Lieberman, 2006). We also investigated using chromosome conformation capture data the physical contacts between the 2µ plasmid and its host's chromosomes, revealing that the 2µ plasmid interacts to discrete positions along their arms. Most contact hotspots consist in long, poorly transcribed regions depleted in proteins of the transcription machinery or known DNA binding complexes, and might be associated with nucleosome signals as it depends on the tail of histone H4. Previous reports have pointed out that the STB region on the 2µ plasmid recruits several yeast factors necessary for its segregation into the two daughter cells, including Cse4  (Hajra et al.
15 M NaHCO 3 , 0. 1% Tween, 0. 05% BSA) for 30 min at 37°C. For the DAPI staining, the slides were incubated in a DAPI solution (1µg/ml in 1× PBS) for 5 minutes and then washed twice in 1× PBS without DAPI. Microscopy and image analysis. For all fluorescent images, the axial (z) step is 200 nm and images shown are a maximum intensity projection of z-stack images. Images were acquired on a wide-field microscopy system based on an inverted microscope (TE2000; Nikon) equipped with a 100/1. 4 NA immersion objective, a C-mos camera and a Spectra X light engine lamp (Lumencor, Inc) for illumination. The microscope is driven by the MetaMorph software (Molecular Devices). Images were not processed after acquisition. Images shown are maximum intensity projection of Z-stack acquisition.
Contact data processing (Hi-C, Micro-C) Hi-C and MicroC processing was performed using hicstuff package  (Matthey-Doret et al. , 2022). Briefly, the paired-end reads were aligned to the S288C reference genome (GCA-000146045. 2, R64-1-1) and the 2µ plasmid sequence (GenBank accession number: CM007980) as well as with M. mycoides (GCA-006265075). For the two experiments containing the lacO site array, reads were aligned to genomes based on strain W303 containing the lacO site sequences (for details on the constructions, see  (Guérin et al. , 2019) ). A threshold of 1 for mapping quality was used for these 2 experiments. Genomes for Lachancea waltii was CBS6430 and X56553. 1 sequence was used for pKW1 plasmid. Genome for Lachancea fermentati was CBS 6772 and M18275. 1 sequence was used for the plasmid pSM1.
ChIP-Seq, Mnase-seq, ATAC-seq and RNA-seq processing ChIP-Seq, Mnase-seq, ATAC-seq and RNA-seq processing was performed using TinyMapper (https://github. com/js2264/tinyMapper). Paired end reads were aligned using the very sensitive and local mode of bowtie2  (Langmead and Salzberg, 2012) , against S288C reference genome and the 2µ plasmid (CM007980) sequences. Only concordant pairs were retained, and reads with a mapping quality larger than 0 were kept. PCR duplicates were removed using samtools. When available, the input was similarly processed. Coverage for each genomic position was computed using deeptools bamcoverage function then normalized by Count Per Million (CPM) method. ChIP signal was then computed by dividing immuno-precipited normalized coverage by input coverage. Signals were then visualized with home made python using pyBigWig package.