dataesr/scientific-paragraphs-categorization huggingface dataset:
Parasitemia Determination-Asynchronous P. falciparum parasites (Dd2 strain) were cultured in standard conditions (as described above), then synchronized twice via sorbitol  (Lambros and Vanderberg, 1979)  and grown to 7% parasitemia at the late trophozoite/ schizont stage. Patient blood was obtained from TSRI Normal blood donor service, washed and centrifuged three times (at 800 × g for 5 min at 4°C) with RPMI only and once with complete media (as above)  (Elias and Greene, 1979) , with any visible buffy coat being removed after each spin. All blood samples were given a numerical designation and allele status was not determined until after all data collection was completed. All genotypes were blinded from experimenters. (Basic Malaria Microscopy, 2010) Parasite growth was determined in two independent ways: absolute parasitemia determination via thin-blood smear after Giemsa staining, and inferred parasite growth via the SYBR Green I-based fluorescence assay  (Johnson et al.
the data are presented as the mean ± SEM or SD and represent at least 3 independent experiments. Statistical analysis, significance level and n values are described in the Figure or Figure legends. For mouse experiments n = number of animals and at least n = 4 were used. For human blood experiment, n = number of individual blood samples. For comparison, we performed two-tailed Student's t-test, where p < 0. 05 is considered statistically significant. For all datasets, we used Prism7 to perform the statistical analysis.
Acknowledgments We thank  Ali Torkamani  for advice on genomics,  Dominic Kwiatkowski  and  Ilya Shlyakhter  for discussions, and  Lisa Stowers  for reading the manuscript. This work was partly supported by grants  NIH   R01 DE022358  to  A. P  and  AI090141  and  AI103058  to EAW. S. M is supported by  Calibr-GHDDI Gates postdoctoral fellowship. GLM is supported by an  A. P. Giannini  postdoctoral fellowship. K. G. A. is a  Pew Biomedical Scholar  and is supported by  NIH   NCATS CTSA UL1TR001114. A. P. is an investigator of the  Howard Hughes Medical Institute. The authors declare no competing financial interests.
Quantification and statistical analysis Statistical analysis-All of the data are presented as the mean ± SEM or SD and represent at least 3 independent experiments. Statistical analysis, significance level and n values are described in the Figure or Figure legends. For mouse experiments n = number of animals and at least n = 4 were used. For human blood experiment, n = number of individual blood samples. For comparison, we performed two-tailed Student's t-test, where p < 0. 05 is considered statistically significant. For all datasets, we used Prism7 to perform the statistical analysis.
PCR, DNA Sequencing and Mutation Analysis Amplicons corresponding to the MT-CO1 and MT-RNR2 genes had been PCR amplified and Sanger sequenced, with methods and results reported previously. 10 The original Sanger sequencing cohort contained POAG cases, controls, and some glaucoma suspects (excluded from analyses). Because some subjects' disease had progressed since the cohort was sequenced, subject disease status was updated to be current as of August 2016, before reanalysis for association with POAG by sex, and data from more recently enrolled subjects were included (total n ¼ 2189; 1339 cases, 850 controls). Version 5. 2 of Sequencher software was used to score sequencing chromatograms for missense variants. Missense variants were annotated using version 1. 0 of the MitImpact resource (available in the public domain at http://mitimpact.
Phylogeny and Localization of Amino Acid Replacements Build 17 of PhyloTree (available in the public domain at www. phylotree. org) and the MITOMAP and MITOMASTER databases (available in the public domain at www. mitomap. org) were used to associate the observed mtDNA variants with mitochondrial haplogroups. The subcellular localizations of MT-CO1 missense mutations were predicted by sequence analysis using the UniProt resource (available in the public domain at www. uniprot. org).
Phenotypic Characterization of V83I POAG Cases Phenotypic data for 1070 AA POAG cases were extracted from the POAAGG study's Research Electronic Data Capture (REDCap) database,  18  and cases were grouped for analysis by MT-CO1 genotypes: m. 6150G (V83, wild type), m. 6150G>A (V83I mutation), m. 6548C (V83 wild type), and m. 6548C>T (V83 wild type, with silent substitution, L215L, indicative of African haplogroup L1b). The masked comparison included 29 cases with V83I and M117T (''double missense'') mutations, associated with mtDNA haplogroup L1c2. ''Triple missense'' cases, possessing a third variant, V193I, associated with an L1c2 sublineage, L1c2b1b, were excluded. The control group included 29 cases with the L1b-associated silent substitution (m. 6548C>, L215L) and lacking all three missense variants.
Each L1c2 case was paired with an L1b control case having the same sex and reported family history of glaucoma (yes or no), and similar age at enrollment into the POAAGG study. Haplogroup was masked during chart review. The following data were collected from the medical records: demographic characteristics, including age, sex, family history of glaucoma; glaucoma phenotypes at visit closest to enrollment, including ICD-9 codes for glaucoma severity, maximum IOP, visual acuity, central corneal thickness (CCT), retinal nerve fiber layer thickness (RNFLT) on OCT, CDR, mean deviation (MD), and pattern standard deviation (PSD) on Humphrey visual field test. A 20% cutoff for false-positive and false-negative response rates and fixation losses was used for visual fields, and the OCT with the best signal strength (minimum 7/10) was chosen for each patient.
Yeast Two-Hybrid (Y2H) Studies All Y2H studies were performed by Hybrigenics Services (available in the public domain at www. hybrigenics-services. com, Paris, France). The coding sequence of the human MT-CO1 fragment (amino acids 41-101, GenBank accession number gi: 251831109) was PCR-amplified and cloned into vectors pB27 and pB66 as C-terminal fusions to LexA (LexA-CO1) and the Gal4 DNA-binding domain (Gal4-CO1), respectively. Codon use of the CO1 insert was optimized for yeast expression, and to prevent spurious amino acid changes caused by differences in the human mitochondrial and yeast nuclear genetic codes. The constructs were checked by sequencing, and used as a bait to screen a random-primed Human Adult Brain cDNA library constructed 19 into pP6, using RNA from a 27-year-old male (#D6030-15, lot A308079; Invitrogen, Carlsbad, CA, USA).
Vector pB27 derives from the original pBTM116 vector, 20 pB66 derives from the original pAS2 DD vector  21  and pP6 is based on the pGADGH plasmid. 22 he N-Gal4-CO1-C bait construct was used for the cDNA library screen, and 47 million clones, 4-fold the complexity of the library, were screened using a mating approach  20  with YHGX13 (Y187 ade2-101::loxP-kanMX-loxP, mata) and CG1945 (mata) yeast strains. A total of 323 Hisþ colonies were selected on a medium lacking tryptophan, leucine, and histidine. The prey fragments of the positive clones were amplified by PCR and sequenced at their 5 0 and 3 0 junctions. The resulting sequences were used to identify the corresponding interacting proteins in the GenBank database (NCBI), using a fully automated procedure. A confidence score (predicted biological score [PBS]) was attributed to each interaction as described previously.
19 The prey fragment for the human APP gene corresponded to amino acids 672 to 713, GenBank accession number gi: 228008403, corresponding to Ab (1-42). The prey fragment was cloned in frame with the Gal4 Activation Domain into plasmid pP7. The pP7 prey plasmid is derived from the pP6 plasmid. The coding sequence of the mutant (V83I) CO1 fragments (amino acids 41-101) was synthesized and cloned in frame with the Gal4 DNA binding domain into plasmid pB66 (N-Gal4-CO1mut-C). The constructs were checked by sequencing the entire insert. The prey fragment for UBQLN1 (amino acids108-388, GenBank accession number gi:194328681) was obtained from the ULTImate Y2H screening of wild type CO1 (amino acids 41-101) against the Human Adult Brain cDNA library. The bait constructs, LexA-CO1, Gal4-CO1, and Gal4-CO1mut, were transformed into the yeast haploid strains L40 DGal4 (mata) and CG1945 (mata) respectively.
Characteristics of the Study Population Phenotypic data from 1070 AA POAG cases were analyzed, and the majority (59%) of these subjects were female. The mean age of wild type (V83) cases was 71. 1 years, vs. 70. 8 years for V83I cases (P ¼ 0. 81). Sanger sequencing of the m. 6150 region of MT-CO1 was obtained for 2189 AA individuals (1339 POAG cases, 850 controls), and 4. 5% possessed the V83I mutation. The 850 non-POAG controls also were mostly female (65%), but significantly younger (61 6 11. 8 years, P < 0. 001) than the POAG group. The total number of cases sequenced was larger than 1070 because an additional 269 cases were sequenced subsequent to phenotypic analysis.
Acknowledgments The authors thank  Jie He  for assisance with sequencing mtDNA, and  Gui-shuang Ying  and  Rebecca Salowe  for helpful suggestions during preparation of the manuscript. Supported by the  National Eye Institute  ( 1RO1EY023557-01 ),  National Eye Institute Vision Core Grant  ( P30 EY01583-26 ), and  National Institutes of Health (NIH ;  LM010098 ;  Bethesda, MD, USA) , as well as the  F. M. Kirby Foundation, Research to Prevent Blindness ,  UPenn Hospital Board of Women Visitors ,  Paul Mackall  and  Evanina Bell MacKall Trust , and  National Eye Institute, NIH, Department of Health and Human Services , under eyeGENE (contracts # HHSN260220700001C  and  HHSN263201200001C ), and the  Department of Ophthalmology at the University of Pennsylvania. Disclosure:  D. W. Collins , None;  H.
There is increased demand for dairy products worldwide, which is coupled with the realization that consumers want dairy products that are produced in a sustainable and environmentally benign manner. Forage legumes, and white clover (Trifolium repens L. ; WC) in particular, have the potential to positively influence the sustainability of pasture-based ruminant production systems. Therefore, there is increased interest in the use of forage legumes because they offer opportunities for sustainable pasture-based production systems. A meta-analysis was undertaken to quantify the milk production response associated with the introduction of WC into perennial ryegrass swards and to investigate the optimal WC content of dairy pastures to increase milk production. Two separate databases were created.
In the grass-WC database, papers were selected if they compared milk production of lactating dairy cows grazing perennial ryegrass-WC (GC) swards with that of cows grazing perennial ryegrass-only swards (GO). In the WC-only database, papers were selected if they contained milk production from lactating dairy cows grazing on GC swards with varying levels of WC content. Data from both databases were analyzed using mixed models (PROC MIXED) in SAS (SAS Institute, Cary, NC). Within the grass-WC database, where mean sward WC content was 31. 6%, mean daily milk and milk solids yield per cow were increased by 1. 4 and 0. 12 kg, respectively, whereas milk and milk solids yield per hectare were unaffected when cows grazed GC compared with GO swards. Stocking rate and nitrogen fertilizer application were reduced by 0.
Literature Search, Data Criteria, and Database Design An electronic literature search [Web of Science (http:// thomsonreuters. com/ web -of -science/ ) and Google Scholar (http:// scholar. google. com/ )] was conducted to identify papers for data extraction in which the effect of WC inclusion on milk production in lactating dairy cows was studied. The search was undertaken using the following key words in different combinations: white clover, milk production, perennial ryegrass, grazing, and dairy cow. More papers were identified by reviewing the reference list in the publications resulting from the search. These papers were also used to study the effect of differing sward WC content on milk production. Papers were selected if (1) they compared milk production from a GC sward with that from a GO sward, (2) lactating dairy cows were under strip or rotationalgrazing management, and (3) they compared at least 2 WC contents under similar experimental conditions.
The inherent management associated with GC swards (i. e. , reduced stocking rate and N fertilizer application rates) made it difficult to locate data, so a decision was made to compare milk production from GO and GC swards under different stocking rate and N fertilizer regimens with all other experimental conditions the same to have sufficient data. After accounting for publications with duplicate data or insufficient information provided, a starting database was constructed. The database was conceptualized with rows representing treatments within an experiment and columns reporting treatment characteristics and least squares means of measured variables. All papers were organized by author name(s), year of publication, and publishing journal. Each comparison of GO versus GC within an experiment was allocated an individual experimental code or study effect to account for the variation between experiments not explained by WC content.
Experimental characteristics required included experimental design, number of cows, experimental duration, number of treatments, stocking rate, grazing days per hectare (Gd/ha), lactation length, WC content of the sward, milk production, BW results per cow and per hectare, and sward nutritive value results. In experiments in which a subfactor was studied (e. g. , at 2 supplementation levels or at 2 postgrazing sward heights) or multiple years of data were reported, comparisons of milk production from GC and GO swards conducted under similar experimental conditions were considered as independent studies.
Calculations Occasionally, measured variables such as solids-corrected milk (SCM) yield per cow or Gd/ha were not reported and were subsequently calculated according to the following formulas: SCM yield/cow = (12. 3 × fat yield) + [6. 56 × (protein yield + lactose yield)] -(0. 0752 × milk yield), Gd/ha = (no. of cows × grazing experiment length)/ area used during grazing experiment (ha), Also, within the database, variables were not consistently reported in each experiment. Similar to  Mc-Carthy et al. (2011) , milk yield per cow per day was reported, and milk yield per hectare was then derived according to the formula Gd/ha × milk yield/cow per day = milk yield/ha.
Requirements For analytical purposes, 2 main subsets of data were created. The grass-WC (GWC) database included experiments that contained a GO treatment that was taken as the base milk production that swards containing WC could be compared with. Grass WC database experiments reflected the milk production effect of introducing WC into a GO sward. The GWC database contained 15 papers and 35 comparisons of milk pro-duction from GO and GC swards published between 1985 and 2015. As the objective of the study was to analyze the effect of WC inclusion in a PRG sward, within the database, the GO treatment within each experiment was considered the base level of production, with the milk production at this WC content (i. e. , 0%) considered as base milk production. By selecting this variable to standardize measurement methods across experiments, it allowed the identification of the true effect of the inclusion of WC from a range of experiments that included large variations in experimental conditions.
As all experiments did not contain a GO treatment, treatments that had a sward WC content below 5% were considered as the GO treatment and were considered the base WC content, and the milk production at this WC content was considered as the base milk production. The second database, the WC-only (WCO) database, included experiments without a GO treatment, with GWC database GC treatments included. The aim of this database was to predict the resultant milk production effect of varying sward WC content. In the WCO database, papers were selected if they contained milk production from lactating dairy cows grazing on GC swards with varying levels of WC content. The WCO database was constructed similarly to the GWC database. These WCO database experiments reflect the overall effect on milk production as WC content increases in the sward.
Statistical Analysis Individual variables (e. g. , milk yield, MS yield) from the GWC database were analyzed using linear mixed models (PROC MIXED) in SAS  (SAS Institute, 2006). Terms included in the model were treatment (GC or GO) and the study effect (individual experimental code), which represented the variance between studies not accounted for by the variables in the model, as described by St Pierre (2001) and more recently  Sauvant et al. (2008). The study effect was included as a random effect, and an unstructured variance-covariance structure among records was used. Significance was declared at P < 0. 05, and tendencies were declared at 0. 05 < P < 0. 1. Publication bias was assessed using funnel plots as described by  Lean et al. (2009)  and the publication bias test (metabias) using the R package meta (version 4.
0-2;  Schwarzer et al. , 2015). Within the WCO database, per-cow data were analyzed using linear mixed models (PROC MIXED) in SAS  (SAS Institute, 2006) , with WC content (CC) as a continuous variable included as a fixed effect, the study effect (individual experimental code) included as a random effect, and an unstructured variance-covariance structure among records according to Equation  1 : Ry = a + study + b × CC + c × CC 2 , [1] where Ry is the predicted production of variable y in response to WC content change, a is the intercept, study is the study effect, b represents the linear coefficient, and c represents the quadratic coefficient. Where c was observed to be greater than P = 0. 10, it was removed from the analysis. Per-hectare data within the WCO database were analyzed similarly (Equation  2 ).
Study was excluded and Gd/ha was included in the model, and only experiments with an experimental length greater than 150 d were used for the analysis to obtain an interpretative relationship between production per hectare, sward WC content (CC), and Gd/ha due to the very important role of Gd/ha in per-hectare performance: Ry = a + b × CC + c × CC 2 + d × Gd/ha, [2] where Ry is the predicted production of variable y in response to WC content change and a Gd/ha change, a is the intercept, b represents the linear coefficient, c represents the quadratic coefficient, and d represents the coefficient of Gd/ha. A further analysis of the WCO database was undertaken to try to investigate the interactions between sward WC content (CC), stocking rate (SR), Gd/ha, and N fertilizer. Milk and MS yield per cow were analyzed according to Equation  3 : Ry = a + b × CC + c × (SR or Gd/ha) + d × N, [3] where Ry is the predicted variable y in response to WC content, stocking rate, and N change; a is the intercept; b represents the linear coefficient for WC content; c represents the coefficient for stocking rate for per-cow variables and the coefficient for Gd/ha for per-hectare variables; and d represents the coefficient for N fertilizer application.
Effect of WC Inclusion in PRG Swards on Milk Production Within the GWC database, the total number of data comparisons for milk production from a GO sward and a GC sward was 35. The mean experimental character- istics and milk production per cow and per hectare for the GWC database were as follows: number of cows = 44, stocking rate = 3. 45 cows/ha, experiment length = 104 d, N application = 105 kg/ha, WC content = 16. 2%, daily milk yield/cow = 19. 0 kg, and milk yield/ ha = 8,984 kg. The average effect of introducing WC into a PRG sward on milk production per cow and per hectare for the GWC database is outlined in Table  1. Perennial ryegrass-WC treatments had a lower stocking rate (cows/ha) and lower rates of N fertilizer application (kg/ha) compared with GO treatments (3. 57 cows/ha and 146 kg of N/ha compared with 3.
32 cows/ ha and 65 kg of N/ha for the GO and GC treatments, respectively). Within the GWC database, mean daily milk and MS yield per cow and Gd/ha for the GO treatments were 18. 3 kg, 1. 36 kg, and 468 d, respectively. When WC was introduced, mean sward WC content was 31. 6%, GD/ha was 440 d, and mean daily milk and MS yields per cow were increased (P < 0. 001) by 1. 4 and 0. 12 kg, respectively. However, milk and MS yields per hectare were unaffected when cows grazed GC swards compared with GO swards, although there was a numerical reduction of 213 and 20 kg, respectively. Introducing WC into the sward resulted in a significant increase (P < 0. 001) per cow for daily milk yield (+7. 6%) as well as fat (+9. 1%), protein (+10. 0%), MS (+8. 8%), and lactose (+14. 7%) yields. Similarly, there was a significant increase per cow at the P < 0.
This is similar to  Riberio-Filho et al. (2003)  and  Egan (2015a) , who reported that WC inclusion in PRG swards increased milk production by 1. 0 to 1. 8 kg/cow per day when cows grazed GC swards compared with GO swards. This increase in milk production per cow was reliable when grazing GC swards, as 86% of the experiments within the GWC database reported an increase in milk production when cows grazed GC swards. This is illustrated in Figure  2 , which shows the relationship between milk production per cow from cows grazing GO and GC swards, as the majority of the data points are above the x = y line. However, in 14% of the experiments, there was no effect or a reduction in milk production per cow when cows grazed GC swards compared with GO swards. This is illustrated in Figure  3 , which shows the variable response to sward WC inclusion within experiments in the GWC database.
Milk fat and protein contents were unaffected by WC inclusion (P > 0. 1). This is similar to Enriquez-Hidalgo et al. (  2014 ) but in contrast to  Woodfield and Clark (2009) , who stated that WC inclusion tended to decrease milk fat content and increase milk protein content. The lack of an effect of WC on milk production per cow has also been reported previously  (Ledgard et al. , 1998; Humphreys et al. , 2009; Enriquez-Hidalgo et al. , 2014)  and may be related to sward WC content. Andrews et al. (2007)  stated that a sward WC content of >20% is required to see an animal production effect, although within the GWC database, experiments that saw an increase in milk production per cow had a mean sward WC content of 35%, whereas experiments that saw a decrease in milk production per cow had a mean sward WC content of 29%.
When productivity was measured on a per-hectare basis, in the GWC database, milk and MS yields were not reduced, although stocking rate and N fertilizer application rate were reduced by 0. 25 cows/ha and 81 kg/ha, respectively, for cows that grazed GC swards compared with GO swards. Within grazing systems, stocking rate, milk yield per cow, and milk yield per hectare are closely linked  (McCarthy et al. , 2011). Generally, as stocking rate increases milk yield per cow decreases and milk yield per hectare increases  (Macdonald et al. , 2008) , and vice versa. The increase in daily milk yield per cow for cows grazing on GC swards was partly attributable to the reduction in stocking rate and partly attributable to the presence of WC in the sward. Using the stocking rate effect prediction equations of  McCarthy et al.