dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, our pipeline can be applied to many existing datasets. In our study system, the sheltered nature of sites excluded potential effects of rain or direct sunlight. Adding such environmental factors or information on whether samples were collected in sheltered or open locations could further improve predictions, because cover not only protects from rain  (Agetsuma-Yanagihara et al. , 2017)  and sun  (Vili et al. , 2013)  but could also limit drying  (Sawaya et al. , 2015). The duration of sample exposure (i. e. , "sample age") is an important, yet rarely known variable. In our case study, we used maximum possible sample age instead (in the form of exposure time, "Days"). In other systems, maximum possible sample age can be assessed by regular transects to discover new samples, or based on the decrease of the eRNA:eDNA ratio with increasing age  (Marshall et al.
, 2021). Where samples are not clustered in well-defined units like roosts, samples can be pooled within a similar environment (e. g. , transect IDs, multiple water samples, building) and according to the closest weather station or data logger. If environmental conditions within a pool vary too much, misrepresentation and therefore lowered accuracy of model estimates and predictions can occur as seen in three out of four RVs in the test dataset (Figure  4a , Test data). When pooled appropriately, the degrading effect of the environment on mean eoDNA quality can be quantified, even when the exact exposure timing is unknown. To capture environmental eoDNA degrading effects and improve predictions, the selection of different pools should also reflect the variability, range, and combinations of environmental variables within a system, as seen in the RVs of the case study spanning from dry and hot attics to humid and cold basements (Figure  4b -g; Appendix S1: Supp_12).
2), unlikely to affect the overall outcome, as glmms are robust against such violations (Schielzeth et al. , 2020)  and continued with Step 4. F I G U R E 1 Seven-step-modeling-pipeline to quantify environmental factors degrading extra-organismal DNA and predict site-specific ideal sampling duration (central part), and their involved datasets. (a) Steps 1-3 outline the process to create a final global model that fulfills model assumptions, (b) Steps 4-7 describe the process to generate reliable model estimates and evaluate a predictive model based on the final global model produced in part a. Box width indicates the qualitative number of models involved; a narrowing box indicates a decrease, widening an increase, of the number of models involved per procedure. Folds in cross-validation are generated according to the 21 sampling visits in the model-building dataset.
Bold outline indicates the path followed for the case study. TA B L E 1Overview of the model-building dataset, including the data type, description, and sample size for each considered variable. Corrective model structure resulted from the automated correction performed in Step 3 of the modeling pipeline. "Z-scored" indicates variable standardization by two standard deviations and centering at 0. Variables of fixed effects and corrective models were always created per roost-visit (RV, 21 values), and are equal for all droppings within the corresponding RV. However, the response variable QI can differ between droppings of the same RV.
F I G U R E 4 4 Overview of observed QI and environmental conditions and predicted QI based on both the final global model and predictive models for each roost-visit (RV). The 21 RVs used for model building are denoted with a gray background, and the four RVs used as test dataset are on the light brown background. (a) Observed and predicted mean Quality Index (QI) per RV. Names along the x-axis follow the format "Roost"/"Year"/"Sess. " RVs are sorted in increasing order according to their mean observed QI (gray plus). Light blue distributions indicate observed QI density (Horizontal ticks indicate the lower limit of the observed QI), medium blue the mean predictions based on the full model, dark blue the predictions based on the full model without using random effect variance (no RE), and orange the predictions based on cross-validation.
"Full" indicates predictions are based on the full model-building dataset, unlike in cross-validation, where they are based on the full model-building dataset minus one RV for every RV, Like-colored symbols indicate AICc-averaged predictions of the 19 top models. Red asterisks indicate mean predictions per RV of the best predictive model (lowest mean squared error (MSE) under crossvalidation). (b, c, e-g) Unstandardized temperature (reddish) and humidity (bluish) predictors experienced during the duration (d) of a RV used in the models. Solid lines connecting these predictors indicate fixed; dashed lines indicate corrective model predictors. Connections do not imply interpolation. (d) Also shows the estimated ideal sampling duration to reach a QI of 0. 95 using the single best predictive model in green.
sources and study goals in existing and new datasets, ultimately improving sampling efficiency and accuracy of information gained from genetic material left behind by organisms in their environment. AUTH O R CO NTR I B UTI O N S E. J. P. and S. J. P. designed the project. S. J. P. initiated environmental logging in 2015. Project administration was done by J. S. and S. J. P. T. N. and L. L. collected samples. T. N. , L. L. , and A. -L. B. extracted and genotyped DNA. T. N. developed and applied the modeling pipeline.
New tools for accurate variant's classification Nowadays, the major challenge for the molecular geneticists in charge of PGL diagnosis remains to correctly classify molecular variants identified by NGS. In 2017, the NGS in PPGL (NGSnPPGL) Study Group (ENS@T/ PRESSOR) published standardized recommendations and initiated an international effort to collect, annotate, and classify variants in order to develop gene-centric curated database of PGLs  (The NGS in PPGL (NGSnPPGL) Study Group et al. 2017). Following this initiative, 223 SDHB variants from 737 patients were collected worldwide and manually curated by a panel of experts from the NGSnPPGL Study Group who established a consensus classification. This curation reduced by half the variants initially classified as variants of unknown significance (Ben  Aim et al.
2022). The SDHB variants classified by these experts are now freely available and publicly accessible via the Leiden Open Variation Database (LOVD) system (https://databases. lovd. nl/shared/genes/SDHB). There have also been advances in our annotation of TMEM127 variants. Recent structure-functional studies revealed additional features of TMEM127, including a fourth transmembrane domain and an endocytic domain  (Flores et al. 2020). These findings provided the basis for preliminary evaluation of a new classification of variant pathogenicity in 111 carriers and support the location of nonconserved missense mutations in transmembrane domains as a likely feature of pathogenic variants  (Armaiz-Pena et al. 2021)  Most of these variants have been deposited in LOVD (https://databases. lovd. nl/shared/ genes/TMEM127).
Future of clinical trials in PGL Lastly, the next big frontier in PGL research will come from new therapies based on strong biological rationale and which are being tested in new clinical trials. The results of the first international randomized study in metastatic progressive PGL (FIRSTMAPPP, NCT01371201) investigating sunitinib (37. 5 mg/day) or placebo were first presented during European Society of Medical Oncology Congress 2021 and at ISP2022. The median PFS was 8. 9 months in the sunitinib arm vs 3. 6 months in the placebo. This academic double-blind trial, in which 78 patients were enrolled, provided the highest level of evidence available thus far and established sunitinib as the first-line option for affected patients with progressive metastatic PGL  (Baudin et al. 2021). On the heels of its approval by the FDA for the treatment of VHL-related tumors  (Jonasch et al.
Plasma guselkumab levels were consistently higher in the PASI = 0 group. Multiple logistic regression analysis revealed absence of diabetes, lower Dermatology Life Quality Index score at baseline, and higher Week 4 guselkumab plasma concentration as significantly (p < 0. 05) associated with the PASI = 0 group. Conclusion A substantial (22. 7%) number of guselkumab-treated patients in the VOYAGE 1 clinical trial maintained complete skin clearance for a consecutive period of ≥ 156 weeks. Factors associated with this outcome may suggest clinical benefits of holistic treatment approaches. Trial Registration NCT02207231.
Methods This exploratory post-hoc analysis was performed using the full 5-year dataset of the VOYAGE 1 clinical trial (ClinicalTrials. gov identifier NCT02207231)  [11]. Data from patients treated with guselkumab from both Week 0 (guselkumab arm) and Week 16 (placebo crossover arm) who maintained PASI = 0 at all visits for ≥ 156 consecutive weeks of treatment (PASI = 0 group) were compared with data from patients who never achieved PASI = 0 at any visit (comparator group) (Fig. 1 ). For purposes of assigning participants into these two response groups, missing PASI values were considered to not be 0. Study visits were scheduled at Week 0, Week 2, Week 4, then every 4 weeks up to Week 52, and then every 8 weeks through to the final visit at Week 252. Of note, as this study ended at Week 252, the initial PASI = 0 response needed to occur within the first 96 weeks or first 80 weeks of guselkumab treatment for those starting at Week 0 or Week 16, respectively, in order to fulfil the requirement of at least 156 consecutive weeks of complete skin clearance.
A previous report investigating the effects of diabetes on guselkumab plasma concentration demonstrated 12% higher apparent clearance of guselkumab in patients with this metabolic disorder, indicating marginally reduced guselkumab  From Week 28 onwards, only trough levels were assessed. Data from visits where n ≤ 3 have been excluded. GUS guselkumab, PASI Psoriasis Area and Severity Index exposure in diabetic patients  [31]. Among those receiving guselkumab for treating psoriatic arthritis, diabetes and body weight have also been identified as factors contributing to pharmacokinetic variability of treatment  [32]. Similar reports with other biologics, such as ustekinumab, demonstrate increased clearance in patients with diabetes, and real-world evidence has demonstrated a lower response to risankizumab in patients with a BMI ≥ 30  [33] , suggesting that this association may be more broadly associated with biologic therapy  [34].
Data We collected 45 genome-wide SFS that are described in Tables 2 and A. 5. The collected SFS come from public data sets or private communications. For 20 data sets, SFS were extracted from whole genome SNP data, including both coding and non-coding regions. For 16 data sets, they were extracted only from transcriptomes (equivalent to coding regions). For 9 bacterial data sets, the SFS were extracted from the core genome. Supplementary files 1 and 2 provide the shapes of the empirically-observed SFS.
Statistical performance Using simulations, we first assess the power of the method to retrieve the correct model and then its power to estimate the parameters. Briefly, for each simulation, we simulated 100 independent loci for each parameter combination, sampling over the coalescent parameter (α or Ψ), the growth rate of the demographic model (g), and the misorientation probability (e). For each locus, we then simulate SNPs under an infinite sites model, with a mutation rate such that on average 50 sites are segregating for each locus. This simulation setup is described in further detail in Appendix A. 5. Applied to the simulated data, our method performs well. Even for small datasets (n = 25), the model selection approach based on Bayes factors computed from Eq. (2) identifies the correct multiple merger model in most cases (Table  1 ), as long as multiple mergers occur with reasonable frequency.
Data analysis The simulations demonstrate that the method is able to retrieve the correct model, and also correctly estimate the parameters of the MMC, provided that there is enough signal in the data. Next, we applied the method to 45 real SFS from 45 distantly related taxa. We first tested how many datasets are better fit by an MMC model than by a Kingman model, then tested the goodness of the MMC fit and estimated MMC parameters for real data. MMC fits better than Kingman. First, we assessed the fit of each SFS to both MMC models and the Kingman coalescent, with exponential growth and misorientation. Using the Bayes Factor criterion, we selected the best fitting model for each empirical SFS in our dataset (Table  2 ). A large majority (73%) of the SFS produce a better fit to MMC models than to the standard Kingman coalescent model.
We observe that the growth parameters are often higher when inferred under the the Kingman coalescent than in either of the MMC models (Table  A. 4), although estimates of g tend to converge in empirical datasets where the MMC parameter estimate approaches  Kingman (Fig A. 5a ). This mirrors previous results of compensating the effect of MMC when inferring under a Kingman coalescent by estimating a higher growth rate in our scenario without allele misorientation, see e. g. [MHAJ18]. , In contrast, the allele misorientation parameters e are almost identical between the Kingman model and the MMC (Fig A. 5b ), which may be a consequence of adding a second, coalescent-model-free estimation method for e to the pseudolikelihood 2. This suggests that for datasets with frequent multiple mergers, assuming a Kingman model may lead to overestimating g, but is not likely to impact estimates of e.
This is not always true for MMC models (see  [BBE13]  and Appendix A. 8), especially for Psi-coalescents caused by strong sweepstake reproduction events with Ψ well above 0. Thus, the real error rates of our techniques could be higher than anticipated by our simulation study. However, this potential increase in error rates can be offset by the presence of datasets that are larger than those assumed in our simulation study. Additionally, due to our reliance on the expected SFS entries -which are averages over the tree space -our inference method (and also our goodness-of-fit assessment) should perform worse (given identical sample sizes and mutation counts) when used on species with small genomes and low recombination rates. This tendency is clearly visible in the goodness-of-fit tests of multiple bacterial data sets.
However, other potential factors can also influence the SFS and produce SFS with similar shapes. We further discuss here two particularly notable factors, population structure (e. g. gene flow or admixture) and biased gene conversion. First, to explore population structure, we performed a PCA analysis of all datasets, followed by a k-means clustering (results in Table A. 6). Importantly, among the 11 species that display a clear pattern of genetic structure, only 6 have an observed U-shaped SFS that is well fitted (grades A and B) by an MMC model. Furthermore, among the 14 species with no clear structure, 10 have an observed U-shaped SFS well fitted by an MMC model. This suggests that population structure is not the main cause of the U-shape of the observed SFS. Additionally, many species with clear structure have low goodness-of-fit grades (C and D), suggesting that none of the models we compare are a good fit to these datasets.
A. 10 Nucleotide diversity across the genome We recorded the (sample) mean and standard deviation of the per-site nucleotide diversity in non-overlapping windows of 15,000 sites along the genome (resp. the sequenced part of it). For computation, we used the R package pegas  [Par10]  and vcftools [DAA + 11] (for haploid data presented as vcf files, we used J. Dutheils fork of vcftools https://github. com/jydu/vcftools). For the human data, we omitted the X and Y chromosomes and for D. melanogaster, we used chromosomes 2L, 2R, 3L, 3R.
This is the case for all non-extreme demographies with bounded past population size, since all of them have finite expected TMRCA. A. 12 Psi-coalescent graphs      *Core genomes were computed and aligned as is [RdSB + 18], with the difference that for every species we added to the set of a species genome one further genome from the closest species (to be able to orient the changes). https://github. com/harvardinformatics/shortRead_mapping_variantCalling +SFS were extracted from transcriptomes as described in [RGB + 14].
In addition to one molecule of the ruthenium containing complex, the motif in 5 was found to include half of a molecule of benzene (disordered in a 75 : 25 ratio) proximate to a crystallographic inversion centre. Distance and ADP restraints were employed in this disordered region to assist convergence. The hydride ligand was readily located and, subsequently, refined without restraints. Deposition Numbers 2219479 (for 2), 2219480 (for 3), 2219481 (for 1), 2219482 (for 5) and 2219483 (for 6) contain the Supporting crystallographic data for this paper. These data are provided free of charge by the joint Cambridge Crystallographic Data Centre and Fachinformationszentrum Karlsruhe Access Structures service.
Acknowledgements This work was supported by the  EPSRC  (grant  EP/T019743/1  for AFP) and the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  792674  (FMM). VVI thanks  MINECO/FEDER  ( CTQ2017-83421-P ) and  Gobierno de Aragón (GA/FEDER, Inorganic Molecular Architecture Group   E08_17R ) for financial support and  MINECO/FEDER  for a  FPI  fellowship. We thank  Dr David Liptrot  for assistance with ATR-IR measurements and the anonymous reviewers for valuable experimental suggestions.
Milk miRNome description Characterization of milk miRNomes MiRNA sequencing was performed on samples collected before and during the feed restriction periods, from 3 different milk fractions per trial, and with 8 cows in both trials. All samples combined, an average of 21,679,931 raw reads was obtained per cow, ranging from 266,868 to 57,668,292. After library adaptor removal, size filtering and alignment against the BosTau8 bovine genome, an average of 6,357,421 reads was obtained, ranging from 16,864 to 18,546,736 (Table  1 ). Analysis of these mapped sequences using miRDeep2 enabled the identification of Table  1  Sequencing data regarding microRNAs present in whole milk, fat globules, extracellular vesicles and mammary epithelial cells of bovine milk. Average raw reads (a) and processed reads (b), after the removal of library adapters, size filtering and BosTau8 genome mapping, are described (means with SEM) for trials of high (H) and moderate (M) intensity, before and during feeding restriction 2,896 mature miRNA in total, among which 1,493 were already known in the bovine species, 257 in other species and 1,146 were predicted according to miRBase (v.
22) and RumimiR (v. Jan2020) identifications (Fig. 1 ). Among miRNA known in other species, 27 species were used, with human (hsa), mouse (mmu), goat (chi), rat (rno), sheep (oar), and rhesus macaque (mml) being the most frequently used, in decreasing order. A threshold of 35 reads was identified using the HTSFilter package  [18]  to maximize filtering similarity among the samples, leaving 1,096 informative miRNA whose maximal count across all individuals was higher than 35, including 765 known in bovine species and 118 in other species. During the standard feeding period, in both trials, four miRNA (bta-miR-30a-5p, bta-miR-30d-5p, bta-miR-30e-5p, and bta-miR-148a-3p) were far more abundant than others, in all the fractions combined, representing 41% of all reads. There were 19 miRNA with a read abundance higher than 10,000 reads per million (RPM), representing 76% of all RPM (Fig.
05; Fig. 3C ; Supplementary data S1). In M samples, the miRNome differed significantly among milk fractions (Fig. 4A ) even in the top 10 most enriched sequences (Table  2 ). 1,025 miRNA were present in all fractions, 7 were exclusive to FG (bta-chr10_3293_mt, bta-chr22_23146_mt, bta-chr1_580_mt, bta-chr5_35639_mt, bta-chr29_29795_mt, bta-chr16_12527_mt, and bta-chr19_16187_mt), 6 to MEC exfoliated in milk (bta-Fig. 1  Description of the milk miRNome, all fractions included, without (A) and with (B) filtering sequencing data according to the HTSfilter (35 read threshold). Proportions (%) and number of predicted and annotated microRNAs in all milk fractions from 16 Holstein cows in two trials, according to their annotation in the miRBase (v. 22) and RumimiR (v. Jan2020) databases. A All 2,896 identified sequences.
They only recorded 27 different miRNA reaching this top 10 abundance in all bovine studies, all milk fractions combined, including eight of the top 10 miRNA present in each milk fraction during our study, thus supporting its accuracy. The current study also described new highly abundant miRNA in milk that had not been previously reported, including some that were predicted and not yet included in the miRbase (v. 22) and RumimiR (v. Jan2020) databases (chr4_34135_mt, chr6_38114_mt, and chr8_42219_mt). The different milk fractions all presented a very similar miRNA composition, with 994 out of 1,095 miRNA being common to all fractions across both restriction trials. This agreed with the findings of Li et al. [8]  who observed high similarities between milk fat and whey (87% shared miRNA) but to a lesser extent with milk somatic cells (75% shared miRNA).
Small library preparation and sequencing Small RNA libraries were prepared using the Illumina TruSeq Small RNA Library Prep Kit (Illumina, San Diego, CA) with RNA isolated from the different milk fractions, representing a total of 96 samples. This was performed according to the manufacturer's instructions, with PCR amplification up to 13 cycles, by the GenomEast Platform (IGMBC, Illkirch, France). The single-read sequencing of libraries was carried out on six lanes on an Illumina HiSeq 4000 sequencer by the GenomEast Platform (IGMBC). RNA sequencing data were subsequently deposited in the Gene Expression Omnibus (GEO): GSE207759.
Data analysis Raw sequences were cleaned from adapters and filtered for size  (17-28 nt)  with Cutadapt  [41]. Cleaned sequences were clustered into unique reads and mapped to the bovine reference genome bosTau8 using the mapper. pl module from the miRDeep2 software  [42]. Novel miRNA and precursors were identified using the miRDeep2 core module miRDeep2. pl. Novel miRNA datasets were created by adding miRNA predicted with a miRDeep2 score > 0 to known miRNA (miRBase v. 22  [43] ). Quantification was done using the quantifier. pl miRDeep2 module, and the quantification results were filtered with a custom Perl script parse_miRDeep2_outputs. pl (https:// forge mia. inra. fr/ sylva in. marth ey/ paqmir/ blob/ master/ paqmir_ postp rocess_ quant ifier/ parse_ miRDe ep2_ output. pl) to eliminate any redundancy between known and predicted novel miRNA.
Statistical analysis Statistical analyses were performed using R software v3. 6. 3 (R Development Core Team, 2020, http:// www. Rproje ct. org). The filtering method from the HTSFilter package  [18]  was used to remove miRNA that appeared to generate an uninformative signal. Tests for differential expression were only applied to miRNA whose maximal count across all four samples was higher than the threshold found with HTSFilter. Principal component analyses were performed using the ade4 package v1. 7. 15  [45] , followed by pair-wise differential analyses between the miRNomes of milk sampled before and during feed restriction with the DESeq2 package v3. 11  [46]. MiRNome variations were firstly explored between milk fractions during the standard feeding period, and then between standard and restricted feeding periods within each fraction.