dataesr/scientific-paragraphs-categorization huggingface dataset:
They study how the small set features truly used is often introduced in early version of an API. Nguyen et al. [31]  implement a bytecode based analysis tool to learn API usages of Android frameworks. Their approach is intended to automatically generate recommendations for incomplete API usages, and thus reducing API usage errors and improving code quality. While their dataset covers one application domain, in our paper, we analyze clients of libraries serving different domains. Kula and colleagues  [20]  observe that even if dependency usage is common, maintenance operations on dependencies such as keeping them up to date is often not prioritized. To our knowledge, none of the previous studies has performed on a population as significant as ours, nor proposed the concept of extinction sequences in this context.
Lämmel et al. [21]  perform a large-scale study on API usage analysis based on AST elements migration. This is the work that is the most closely related to ours. Yet it differs in several important aspects. The size and origin of the dataset: we studied a corpus of more than 800,000 clients from Maven Central, i. e. compiled projects. They studied the sources of 6286 projects from SourceForge. We build our dataset by choosing the most popular libraries and then resolve all the clients of those libraries in the ecosystem. We discuss other topics such as bloated dependencies and propose the use of extinction sequences to describe API usages.
Software dependency ecosystems. During the last decade, researchers have investigated the dependency relationships in software packaging ecosystems  [23, 30, 41]. Research efforts focus on the study of library evolution  [11] , updating behaviors  [36]  and the security risks  [48]. Bogart and colleagues  [8]  highlight the different values and customs of different software ecosystems. Raemaekers et al. [35]  constructed a Maven dataset of 148,253 jar files for analyzing the evolution of API members based on code metrics. Gabel et al. [16]  perform a study on the uniqueness of source code showing that most existing code is reused code. Unlike previous work, our study focuses on the analysis of API usages to characterize the reuse-core of API types.
Threats to Validity We report about internal, external, and construct threats to the validity of our study. Internal validity. This study relies on a very rich and complex network of software artifacts. The complexity is such that we could not completely resolve the artifacts captured in the MDG  [7]. Indeed, the MDG contains a minority of artifacts, hosted on other repositories than Maven Central. For network reasons, e. g. download limitations, some artifacts could not be resolved. In total, we resolved 829,410 of the 901,876 artifact (91. 84%), which corresponds to 2,169,273 dependency relationships (91. 78%). Our analysis covered 87,207,807 usages of 5,076,307 different API elements. We believe that the results obtained with this large set of APIs and clients represent a good approximation of how clients use popular libraries.
External validity. Our findings might not generalize to all Java APIs. We selected the 94 LIBs based on their popularity and on the popularity of Maven Central. We also noticed that these APIs cover a variety of usage domains (e. g. , collections, logging, XML parsing). As Maven Central is a collection of opensource components  11  , they may not behave as pure end-applications. All the client artifacts in our dataset are artifacts from this repository. This means that the exact sets of types included in the of the libraries studied in this work could be different when observing a different set of clients. Yet, both Qiu and colleagues  [33] , and Lammel and colleagues  [21] , observe usages distributions that consistent with ours, on a dataset of Java applications mined respectively on GitHub and SourceForge.
For instance, among the 79, 364 clients that declare a dependency toward a version of commons-io, only 47, 495 (59. 8%) refer to an element of its API in their bytecode. Similarly, the of slf4j-api is 67. 3%. This corresponds to 117, 692 clients of 174, 895 containing calls to slf4j-api in their bytecode. 2 https://docs. spring. io/spring-boot/docs/2. 1. 1. RELE ASE/reference/htmlsingle/}configuration-metadata-anno tation-processor 3 https://github. com/ulisesbocchio/spring-boot-secur ity-saml 4 https://github. com/ulisesbocchio/spring-boot-secur ity-saml/blob/master/spring-boot-security-saml/src/mai n/java/com/github/ulisesbocchio/spring/boot/security/s aml/util/ConfigPropertiesMarkdownGenerator. java 5 https://docs. spring. io/spring-boot/docs/2. 1. 1. RELE ASE/reference/htmlsingle/}configuration-metadata-anno tation-processor call to it.
Experimental data The calibration and assessment of the models evaluated in this work were carried out using an experimental dataset obtained from the study of  Moate et al. (2019). The experiment was carried out at the Agriculture Victoria Dairy Research Centre at Ellinbank, Victoria, Australia (38°14 0 37 00 S, 145°56 0 12 00 E). The study involved six rumen-fistulated Holstein dairy cows in late lactation fed either a wheat-based diet (WHT) or a corn-based diet (CRN) in a crossover design. Animals were offered daily a total of 22. 4 kg DM of a diet composed, on a DM basis, of 45. 5% Lucerne hay, 8. 9% coldpressed canola, 0. 5% minerals, 0. 5% molasses, and 44. 6% of either rolled wheat grain or rolled corn grain. Cows were individually offered feed twice daily at $0630 h and 1530 h. Half of the daily feed allocation was offered at each feeding period.
The files conc_XXXYYY. txt in the data repository https://doi. org/10. 15454/CDUKML report the time interval of concentrate feeding and the weights of offered and refused concentrate for the animal XXX and the diet YYY. A full description of the experiment including the DM concentration and chemical composition of the feeds is given in  Moate et al. (2019). The DM values of Lucerne and grains are given in the Scilab file DryMat-terFeed. sci. The feeding regimen used in this experiment, i. e. , feeding the cows concentrate during milking followed by the forage part of the diet after milking, was chosen to replicate the feeding regimens commonly employed on dairy farms in Australia and New Zealand. Feed consumption and methane emissions were simultaneously measured in an open-circuit respiration chamber for 2 days after a period of adaptation of 23 days.
Methane measurements were reported every 12 min. The specifications of the respiration chamber facility are described in  Grainger et al. (2007). Filtering method for intake dynamics DM intake is the primary driver of methane emission and thus an important input of methane models  (Sauvant et al. , 2011; Charmley et al. , 2016). Accordingly, an accurate estimation of DMI is required to provide satisfactory methane predictions  (Appuhamy et al. , 2016). The raw second by second data of feed intake (wet mass) obtained by  Moate et al. (2019)  are noisy and required 'smoothing' for our modelling purposes. We used an automatic algorithm developed on R  (Blavy et al. , 2020)  to filter the raw data from the feed bins. Briefly, the algorithm constructs consecutive plateaus to filter the noise and remove outliers, secondly it detects feed distribution periods and finally it applies the longest decreasing sequence.
The smoothed data (wet mass) were further adjusted to guarantee that the total amount of wet Lucerne consumed corresponded to the difference between the offered and refused Lucerne. We set the algorithm to export the smoothed wet mass values every minute. These values were further used to calculate the wet mass intake for time intervals of 4 min (files WMI_XX-X_YYY in the data repository). These values are further premultiplied by the percentage of DM of the feeds to calculate the DMI.
, 2018). We used the backward differentiation method for the numerical solution of Eq. (  2 ) with a step time of 4 min. The model was implemented in the open source software Scilab (https://www. scilab. org/) and the calibration was performed using the Nelder-Mead algorithm implemented in the fminsearch function. The yield factor Y was calculated directly from the experimental data while the parameter a and the initial condition z 0 were estimated from the calibration routine. The software R (https:// www. r-project. org/) was used to plot the figures of the results.
, 1990). The adjusted initial Intake Rate (IRi) was calculated from Eq. (  3 ): IRi ¼ b Á INTfoMax. We further performed a statistical analysis on the feeding behaviour data with the proc mixed SAS procedure (version 9. 4. SAS Institute, Inc, Cary, NC, USA). Data acquired from a given cow within a diet were considered as repeated means. The model included fixed effects of diet (wheat-or corn-based diet), cow and the interaction between diet type and cow. A principal component analysis (PCA) was also performed on the main behavioural data and parameters of the dynamic model of methane production in order to synthesise the links between feeding behaviour and methane production. We performed a PCA on 13 variables characterising the main behavioural data and those of the dynamic model of methane production in order to synthesise the links between feeding behaviour and methane production.
For each parameter, the mean value of the four successive feed offerings per cow and per diet was calculated. As there were six cows in this experiment and as the experiment had a crossover design (two diets), the experiment provided 12 values per parameter. The 13 variables included in the PCA were as follows: (i) the methane yield (Y, g CH 4 /g DMI) (ii) the eructation rate constant of methane (a, 1/min) (iii) the mean of the data of the four feed offerings per cow of total number of bouts (NBOUT) (iv) the total duration of the eating period (DUREAT, min) (v) the intake rate of forage (QINTfo, g/min) (vi) the duration of the first bout (DUR_B1, min) (vii) the proportion of intake during the first bout (PrINT_B1) (viii) the mean intake rate of this first bout (IR_B1, g/min) (ix) the mean of the data of the four feed offerings per cow of concentrate intake (COint, kg) (x) the satiety fractional rate (b, 1/min) (xi) the initial intake rate (IRi, g/min) (xii) the RMSE of Eq.
Cows fed the CRN diet ate on average, 4. 94 kg DM of Lucerne hay per feeding period (i. e. , 9. 88 kg DM/d) while the cows offered the WHT diet ate 4. 65 kg DM of Lucerne hay per feeding period (i. e. , 9. 3 kg DM per day) and these intakes of Lucerne hay were not different between diets (P = 0. 24). When comparing CRN to WHT, the cows fed the CRN spent less time eating Lucerne   2020 ). (B) Dry matter intake (DMI) of Lucerne (blue circles) calculated using the filtered data and DMI of concentrate (red triangles). For each feeding period, the concentrate was offered to cows during the first 30 min. Then, the concentrate refusals were removed from the chamber and weighted. As the concentrate was consumed very quickly, we assumed a constant DMI during the feeding of concentrate, calculated simply by the ratio between the concentrate consumed (in DM) and the duration of feeding interval.
After removal of the concentrate feed bin in the chamber, a second feed bin with Lucerne was offered. The weight of the feed bins for Lucerne was registered every second in automatic scales. The wet mass records were further filtered and used to calculate the DMI every 4 min. hay, and had a smaller number of eating bouts (Table  1 ). The main response difference between the diets relates to the first bout: three cows made a single bout of eating Lucerne hay after each feed offering on the CRN diet, and out of the 24 data recorded (six cows and four feed offerings per cow and diet), 17 corresponded to a unique bout. With the WHT diet, there were only four data with a single bout during which all or most of the Lucerne hay was consumed. The duration of the first bout tended to be shorter with the CRN diet compared to the WHT diet, but the Lucerne intake rate during the first bout t was greater for the CRN diet than for the WHT diet.
The cow effect was significant, except for the intake and the proportion eaten during the first bout. The interaction between the cow and diet was significant except for the total intake. When the dataset containing the 48 kinetics of forage intake was used to predict IRi, the regression for IRi adjusting the data within cows was as follows: IRi ¼ 155:1 À 28:8½1 for WHT or 0 for CRN À 17:3½number of the successive feed offerings ðfrom 1 to 4Þ; ðn ¼ 48; RMSE ¼ 45:2 g=minÞ ð4Þ All the effects were highly significant including the individual cow effect. For the parameter b, we obtained the following regression: b ¼ 0:026 À 0:0101½1 for WHT or 0 for CRN À 0:0029½number of the successive feed offerings ðfrom 1 to 4Þ; ðn ¼ 48; RMSE ¼ 0:007 min À1 Þ ð5Þ All the effects were highly significant including the individual cow effect.
The first group of variables includes forage intake, proportion of intake during the first bout, adjusted initial intake rate and intake rate in the first bout and methane yield. The second group of variables includes the number of bouts, the duration of eating, the time to achieve 2 kg of eaten forage and the eructation rate constant of methane. The negatively correlated variables are positioned on opposite sides of the plot origin (opposed quadrants). Thus, the PC n°1 represents the forage intake which is closely and positively linked with the initial value of intake rate. Fig. 6  shows that the CRN is eaten in higher quantity with a higher initial IR than the WHT. This difference is significant on the PC n°1 (P < 0. 017, RMSE = 1. 57). Moreover, the dispersion of data of wheat diets on this PCn°1 is much larger (SD = 2.
As observed over the whole dataset, the intake rate during the first bout was greater with the CRN diet, because the cows ate more in a shorter duration, resulting in a greater total intake of concentrate intake compared to the total concentrate intake on the WHT diet. An important difference between diets relates to the different types of starch within wheat and corn grains. The starch in wheat is more quickly fermented within the rumen than corn starch and this may result in sub-clinical acidosis in cows offered wheat-based diets  (Moate et al. , 2019). In this experiment, the nadir in ruminal pH with the WHT diet was approximately 5. 4 while for the CRN diet, it was approximately 5. 8  (Moate et al. , 2019). With the WHT diet, cows tended to counteract the excessive flow of nutrients with this highly degradable starch  (de Smet et al.
Phenome-wide association studies identified numerous loci associated with traits and diseases. To help interpret these associations, we constructed a phenome-wide network map of colocalized genes and phenotypes. We generated colocalized signals using the Genotype-Tissue Expression data and genome-wide association results in UK Biobank. We identified 9151 colocalized genes for 1411 phenotypes across 48 tissues. Then, we constructed bipartite networks using the colocalized signals in each tissue, and showed that the majority of links were observed in a single tissue. We applied the biLouvain clustering algorithm in each tissue-specific network to identify co-clusters of genes and phenotypes. We observed significant enrichments of these co-clusters with known biological and functional gene classes.
E lectronic health records (EHR)-linked biobanks coupled with genome-wide genotyping and sequencing data allows for the study of the impact of genetic variation on thousands of medical phenotypes simultaneously. Phenome-wide association analyses have been conducted in several EHR-linked biobanks and genome-wide association study (GWAS) summary statistics have been made publicly available for large biobanks such as the UK Biobank and FinnGen study. For example, GWAS summary statistics from a phenome-wide scan of the UK Biobank (UKBB)-a prospective cohort with deep genetic and rich phenotypic data collected on approximately 500,000 middle-aged individuals (aged between 40 and 69 years old) recruited from across the United Kingdom 1 -now exists and is a rich resource in the human genetics community.
Results We performed three steps to generate the phenome-wide network map of genes and phenotypes: (1) identification of colocalization signals of eQTLs and GWAS loci for various continuous and binary phenotypes in 48 tissues from the GTEx project v7; (2) construction of a bipartite network using the colocalization signals to establish links between genes and phenotypes in each tissue; and (3) identification of co-clusters of colocalized genes and phenotypes in each bipartite network using the biLouvain clustering algorithm. A graphical flowchart of the study is shown in Fig. 1. Identification of colocalization signals of eQTL and GWAS loci in multiple tissues. We used coloc2  11  , along with GWAS summary association statistics available for 3822 phenotypes in UKBB and eQTL data to identify colocalization signals in 48 tissues from the GTEx project.
Before running coloc2, we performed stringent quality control (QC) on the phenotypes in UKBB. We removed phenotypes related to cause of death and case-control phenotypes with less than 1250 cases (or controls), except phenotypes showing prior gene/locus association as reported in the NHGRI-EBI GWAS catalog. In Neale GWAS data, we retained coloc2 results for 496 continuous and binary phenotypes. In SAIGE data, we excluded case-control phenotypes with less than 200 cases, retaining coloc2 results for 915 case-control (PheCode) phenotypes. We selected variants with minor allele frequency (MAF) > 0. 1% in both Neale and SAIGE datasets; in GTEX, we included cis-eQTL variants with MAF >1% from Analysis V7. We restricted our study to the list of 48 tissues (from 620 donors) having a sample size of at least 80.
Vitamin K-dependent proteins affect a number of physiologic processes including blood coagulation, inflammation, and prevention of vascular calcification  21. Furthermore, a meta-analysis including the UKBB data identified an intronic variant in GGCX associated with coronary artery disease (CAD), with inclusion or exclusion of angina  22. Taken together, these results suggest that the identified co-clusters contain relevant biological information highlighting functional links between genes and phenotypes. Comparison between eQTLGen and GTEX. We compared the colocalizated loci found in GTEX with a larger gene expression level dataset. The eQTLGen Consortium identified cis-eQTLs in blood based on a meta-analysis of 37 different cohorts in up to 31,684 individuals  23. We reran coloc2 using the same GWAS summary statistics (Neale and SAIGE) as before, but this time using cis-eQTL variants from the eQTLGen consortium (see "Methods").
All colocalization results can be found in Supplementary Data 5. As expected, more unique links between genes and phenotypes (2474) were discovered compared to links found using GTEX Whole Blood tissue (1755), most likely due to increased statistical power. However, we found 430 overlapping loci (a single colocalized locus can contain more than one gene) between GTEX and eQTLGen colocalization results in 251 distinct phenotypes (Supplementary Data 6). After running the biLouvain algorithm, we compared the clustering obtained in the colocalized loci from the eQTLGen dataset with those in the GTEX whole blood tissue. All co-clusters in the eQTLGen colocalization results are listed in Supplementary Data 7. Large co-clusters mostly contained the same phenotypes, although the  For the sake of simplification, we grouped phenotypes with similar names in UKBB and/or matching ICD-10 codes and PheCodes.
colocalized genes could differ between eQTLGen and GTEX. One such large co-cluster in eQTLGen (co-cluster 42) includes many of the same phenotypes as in co-cluster 11 in GTEX Whole Blood (Table  1 ), and many genes are common to both co-clusters (DDAH2, FES, PSRC1, FAM177B, RPS12P26, MCL1). Colocalization with disease case sample size larger than UKBB. We assessed the effect of using GWAS summary statistics with case sample size in various diseases larger than in UKBB. Increased case sample size as observed in consortia should in theory generate more colocalized signals than using a population-based cohort such as UKBB. To this end, we evaluated three GWAS meta-analysis case-control datasets as generated by three different consortia: coronary artery disease (CAD) from CARDIoGRAMplusC4D  24  , schizophrenia from the Psychiatric Genomics Consortium (PGC)  25  , and type 2 diabetes (T2D) from DIAGRAM  26.
We reran coloc2 by combining each of these three GWAS datasets with the same GTEX cis-eQTL dataset used before (all 48 tissues). All colocalization results are found in Supplementary Data 8 for CAD, in Supplementary Data 9 for schizophrenia, and in Supplementary Data 10 for T2D. Regarding CAD and T2D, many colocalized loci discovered using either Neale or SAIGE datasets were replicated using the GWAS meta-analysis consortia data, even though CARDIoGRAMplusC4D 24 defined CAD more comprehensively as either myocardial infarction, acute coronary syndrome, chronic stable angina or coronary stenosis >50% (~61,000 CAD cases) while in Neale and SAIGE, such a composite diagnosis is not available, only subphenotypes such as heart attack/myocardial infarction (Neale 20002_1075) or coronary atherosclerosis (SAIGE PheCode 411.
4). In DIAGRAM  26  , the number of T2D cases (~55,000 cases, excluding the UKBB cohort) is about three times the number of cases in Neale (16,183 cases for 2443-Diabetes diagnosed by a doctor) or SAIGE (18,945 cases for PheCode 250. 2-Type 2 diabetes), which enhanced the statistical power to detect more colocalized signals. By contrast, using the GWAS summary statistics dataset from the PGC  25  largely increased the number of colocalized signals compared to using either Neale or SAIGE datasets. No colocalized signal was found with Neale (337 cases for 20002_1289-Self-reported schizophrenia) and only a few using SAIGE (571 cases for PheCode 295. 1-Schizophrenia). The PGC summary statistics (which include ~37,000 schizophrenia cases, about 65 times the number of cases using SAIGE) uncovered many colocalized loci undetected by using either Neale or SAIGE: for example, genes BNIP3L, CNTN4, THOC7, TRPC4, ZNF823, CLCN3, PAX6 were prioritized in the context of synaptic location and function from genome-wide enrichment tests in the latest PGC schizophrenia meta-analysis  27.
Discussion In this study, we have constructed a tissue-level phenome-wide network map, called biPheMap, of colocalized genes and phenotypes using a bipartite network and biLouvain clustering approach on 1411 phenotypes and eQTL data from 48 tissues from the GTEx project. In the phenome-wide map, we observed the following: (1) the majority of colocalized gene and phenotype links are observed in a single tissue, implying that tissue-specific gene regulatory mechanisms drives phenotypic variation; (2) the majority of co-clusters are comprised of a small number of gene and phenotype links; (3) specific co-clusters are enriched with functional gene set annotations; (4) specific co-clusters are identified with biologically relevant gene, phenotype and tissue functions. While most network analyses have focused on unipartite networks, this study used the less familiar bipartite approach.