dataesr/scientific-paragraphs-categorization huggingface dataset:
Our primary goal was not to provide novel estimates of pN/pS but rather to ensure that all population samples have estimates that are consistent with estimates generated from independent Drosophila datasets(Mackay et al 2012). Downloaded from https://academic. oup. com/mbe/advance-article/doi/10. 1093/molbev/msab259/6361628 by Consejo Superior de Investigaciones Cientificas (CSIC) user on 28 September 2021 ratios and numbers of private SNPs (Supplementary Material online, supplementary table To assess whether these features affect basic attributes of the dataset, we calculated six basic quality metrics focusing on the Pool-Seq samples (Figure 1C, Supplementary Material online, supplementary table S2). On average, median read depth across samples is 62x (range: 10-217x). The per-nucleotide missing allele frequency rate was less than 7% for most (95%) of the samples.
18% in 2015/16; t-test p=1. 8x10 -19 ) and DrosRTEC samples (~3% in samples collected as part ofBergland et al. 2014vs. ~14% in samples collected as part ofMachado et al. 2021; p=6. 37x10 -3). Curiously, the 2015/2016 DrosEU samples were made with a PCR-free kit, suggesting that the observed PCR duplicates were optical duplicates and not amplification artifacts. Contamination of samples by D. simulans varied among populations but was generally absent (<1% D. simulans specific reads; supplementary table 1). For the PoolSNP dataset, we varied the global minor allele count (MAC) and global minor allele frequency (MAF) and then calculated pN/pS. MAC thresholds <50 resulted in large variances of pN/pS caused by 20 outlier populations characterized by unusually high pN/pS.
To minimize false positive variant calling, we chose MAC=50 and MAF=0. 001 as conservative threshold parameters for SNP calling with PoolSNP. Using these parameters, PoolSNP identified 4,381,144 polymorphisms segregating among the 271 D. melanogaster samples (Pool-Seq plus DGN), and 4,042,456 polymorphisms segregating among the 246 Pool-Seq samples (excluding DGN). Downloaded from https://academic. oup. com/mbe/advance-article/doi/10. 1093/molbev/msab259/6361628 by Consejo Superior de Investigaciones Cientificas (CSIC) user on 28 September 2021 In contrast to PoolSNP, SNAPE-pooled calls variants in each sample separately using a probabilistic approach which integrates allelic information across all populations for heuristic SNP calling. To quantify the number of putative sequencing errors among low frequency variants we varied the local MAF threshold per sample and calculated pN/pS for each sample in the SNAPE-pooled dataset.
Kofler et al. 2011a; Feder et al. 2012; Schlötterer et al. 2014). Similar to AF estimates, the Pearson correlation coefficients for both coverage and effective coverage were large (0. 92, 0. 95, 0. 90 for Machado et al. (2021), Kapun et al. (2020), and Bergland et al. (2014), respectively; see Supplementary Material online, Figures S7-S12), indicating that sample identity was preserved appropriately. However, the concordance correlation coefficients were substantially lower between the datasets (0. 24, 0. 88, 0. 79, respectively), indicating systematic differences in read depth between the DEST dataset and previously published data. Indeed, Downloaded from https://academic. oup. com/mbe/advance-article/doi/10. 1093/molbev/msab259/6361628 by Consejo Superior de Investigaciones Cientificas (CSIC) user on 28 September 2021 Lin 1989;Liao and Lewis 2000)pun et al.
(2020)Payne). We found that most of the 271 populations were polymorphic for at least one or more chromosomal inversions (supplementary table 1). While most inversions were either absent or rare (average frequencies:In(2R)NS = 5. 2% [± 4. 7% sd], In(3L)P = 3. 1% [± 4. 3% sd], In(3R)C = 2. 5% [± 2. 3% sd], In(3R)K = 1. 8% [± 7. 4% sd],In(3R)Mo = 2. 2% [± 3. 6% sd] and In(3R)Payne = 5. 7% [± 7. 1% sd]), only In(2L)t segregated at substantial frequencies in most populations (average frequency = 18. 3% [± 11% sd]). We found that our novel inversion frequency estimates of the DrosEU data from 2014 were highly consistent with previous estimates fromKapun et al. (2020)as coefficients of determination (R 2 ) ranged from 91% to 99%. Comparison to previously published datasets. We compared the allele frequency and read depth estimates from the DEST dataset (based on PoolSNP) to previously published estimates byBergland et al.
(2014), andKapun et al. (2020),Machado et al. (2021). For these datasets we employed two types of correlations, the nominal correlation (i. e. , Pearson's correlation; CO) and the concordance correlation coefficient (CCC;Lin 1989;Liao and Lewis 2000). The CCC determines how much the observed data deviate from the line of perfect concordance (i. e. , the 45 degree-line on a square scatter plot). (2021)arealsogenerallyhigh(averageCO and CCC >0. 98; Figure5, top row; Supplementary Material online, supplementary fig. S5). AF correlations with the data fromBergland et al. read depth estimates were on average ~12%, ~14% and ~20% lower in the DEST dataset as.
Downloaded from https://academic. oup. com/mbe/advance-article/doi/10. 1093/molbev/msab259/6361628 by Consejo Superior de Investigaciones Cientificas (CSIC) user on 28 September 2021 (red distributions), we show the distributions of correlations estimated with random population pairs (green distributions). Watterson's θ, and Tajima's D, respectively. Higher values of genetic diversity were obtained for the SNAPE-pooled dataset, probably due to its higher sensitivity for detecting rare variants (see Patterns of polymorphism between PoolSNP and SNAPE-pooled). Genetic diversity. We estimated nucleotide diversity (π), Watterson's θ and Tajima's D for both the PoolSNP and SNAPE-pooled datasets (Supplementary Material online, supplementary table S5). Results for the African, European, and North American population samples are presented in Figure 6 (also see Supplementary Material online, supplementary fig.
PoolSNP necessarily has a limited capacity to identify rare and private SNPs because it imposes global minor allele count and allele frequency filters. Therefore, the more populations that are used for SNP calling by PoolSNP, the less likely PoolSNP is to identify Lack et al. 2016), a dataset that amalgamates whole-genome sequencing of inbred line data and haploid embryos from samples collected around the world. Although the DGN data was originally generated by multiple labs and run through a different mapping pipeline than what we used for the Pool-Seq data, these samples appear to cluster tightly with geographically close Pool-Seq samples (Supplementary Material online, supplementary figure Machado et al. (2021))Because PoolSNP filters out rare and private polymorphisms, it is less sensitive to sequencing or library preparation errors.
Notably, the 20 flagged populations do not have elevated pN/pS with MAC > 50. Additionally,Kapun et al. (2020)demonstrated that these problematic samples did not affect population genetic inference based on common SNPs. The problematic samples derived from the DrosRTEC studies likely do not have a major impact on their results either as bothBergland et al. (2014)andMachado et al. (2021)imposed stringent minor allele frequency filters. PoolSNP has the added advantage that it can incorporate in-silico pooled datasets wherein haplotype or genotype information are collapsed into allele frequencies (see Materials and Methods). We took this approach by incorporating the Drosophila Genome Nexus dataset (DGN;.
The VASCERN mobile app The VASCERN mobile app was devised as a modular platform, to be developed throughout the ERN's lifetime via incremental upgrades and gradual inclusion of new services. The basic module of the app was designed to include service finder functionalities (finding the closest HCP or PO for a given RD) and it was built following a pilot experience by members of the eHealth WG, i. e. a mobile app (Explo-Rare) launched in 2015 to support patients with RDs to identify clinical centres in the Lombardy Region in Italy  [18, 34]. Additional modules of the app will be developed in the future including, for example, personalized digital patient passports, tailored educational material, management of emergency calls, and remote consultation. The VASCERN mobile app was launched in January 2019 and is available free of charge on the two leading markets, the iTunes app store and the Google Play store.
The first version of the app has been developed in English and in August 2020 a second version, including multiple EU languages, has been launched. The app includes a repository and service finder functionality to locate the closest HCPs and POs for a given RD in a given geographical area  [34]. Furthermore, to help patients find detailed information about their conditions the app reports the orphacode and a link to the corresponding Orphanet database disease entry page for each RD. In its first version, the app's repository maps all the HCPs that participate in  VASCERN   The app provides simple and essential information. For HCPs, the dataset includes contact details (name, address, contacts, opening hours, coordinator details and specialization), information about the RDs managed by the centre, a list of medical specialties available and services offered (e.
g. , exams, highly specialized diagnostics, treatments, counselling, emergency call centre), and additional resources available (e. g. , websites, social media pages and groups). For POs, the dataset includes contact details (name, address, contacts, opening hours, contact persons), the list of services offered (e. g. , help line, connection to social services, training and education), and links to other related POs. The app also shows cross-links between HCPs and POs that have well established collaborations. The app integrates seamlessly with built-in smartphone services and app callouts to provide related functionalities, for example geolocation, phone calls, messaging, or email. Figure  3  shows an outline of the app functionalities and a typical flow, as shown in the app tutorial: selection of the RD of interest, localization of the HCPs and POs in the area of interest, and retrieval of the related information, indications, and contact details.
shown that successful use of eHealth in the area of RDs requires more than just the development of new tools and that a collaborative approach is key to success. Additional key issues, such as implementation strategies, heterogeneity of systems across countries, legal and ethical concerns, and data protection issues, still need to be addressed to improve coordination and information sharing between HCPs, POs, and policy makers. • The pilot experience of the eHealth WG can serve as a reference model and can potentially be scaled up to leverage the impact of the VASCERN and, more generally, of ERN and international collaborative networks in terms of improved access to diagnosis, treatment, and high-quality healthcare for patients with multisystemic vascular RDs.
Funding This project has been supported by the  European Reference Network on Rare Multisystemic Vascular Diseases (VASCERN) -Project  ID :  769036 , which is partly co-funded by the  European Union  within the framework of the  Third Health Programme  " ERN-2016-Framework Partnership Agreement 2017-2021. This study is partially supported by  Ricerca Corrente  funding from  Italian Ministry of Health  to  IRCCS Policlinico San Donato.
Two different Bayesian inference methods are widely used in order to perform phylogeographic analyses, i. e. , a discrete phylogeography approach  [12]  that uses discrete locations and a continuous phylogeography approach  [13]  for when fine-grained geographic data (e. g. , latitude and longitude) are available. The latter type of information is often available in the case of RABV studies. However, if only the general location is known or the study spans multiple countries, a discrete phylogeographic model is typically used. Both of these approaches have had a strong impact on reconstructing viral spread and are incorporated in the widely-used BEAST software package  [14]. Now that these approaches have been in use for over ten years, we compare both methodologies (see illustration in Figure  2 ) with a view towards answering the following questions: to what extent have these approaches been used to study RABV in different host species, in different parts of the world? What are the different types of conclusions that are reached using discrete phylogeography or continuous phylogeography? What are the advantages and disadvantages of each approach? We also focus on extensions of these phylogeographic models that allow for the use of covariate data by, for example, using generalized linear models.
Compared to the studies using discrete methods, for instance, the geographic areas under consideration in continuous phylogeographic studies are generally much smaller. Furthermore, discrete phylogeographic methods are often employed in preliminary analyses before conducting continuous phylogeographic analyses  [19, 20]. The smaller geographic scope of continuous phylogeographic analyses can be partly explained as a data-availability issue, given that a continuous approach requires precise geographic coordinates. Rabies diagnosis requires a brain biopsy and it is thus difficult to diagnose antemortem  [21]. Most of the time, rabies samples are obtained from roadkill or euthanized suspected rabid animals, and available data are hence sporadic in nature. Reliably tracking the precise movement of rabid animals across vast geographic areas and across political borders depends on sustained surveillance efforts, and would require considerable coordination and cooperation  [16, 22, 23].
For bat RABV data sets however, the meta-analysis showed that anthropological factors did not appear to have been associated with an important impact on the dispersal of those RABV lineages. In Latin America, the common vampire bat is a leading cause of human and animal rabies that result in unvaccinated livestock dying every year  [70, 71]. Owing to the complexity of RABV circulation in bat communities, none of the environmental factors tested by  Dellicour et al. [5]  proved to significantly impact the dispersal velocity of bat RABV lineages in their meta-analysis. One of the explanations put forward by the study was that the factors they tested may not have been the factors that are relevant to the ecology of bats. Sampling bias or sampling from a restricted area within a wider region of bat dispersal could also have compromised the statistical power necessary to identify relevant factors [5].
Download links for BEAST  [14] , BEAGLE (required to run BEAST; Ayres et al. [81] ) and other related packages for processing and visualising the output of phylogeographic analyses can be found on http://beast. community/ last accessed on 10 August 2021 as well. While we discuss the application of phylogeographic methods on rabies virus (RABV) datasets here, it should be noted that these methods could also be used on genomic data from other zoonotic viruses, provided the data contain sufficient temporal signal. We elaborate more about the formal assessment of a temporal signal on available data in the next section.
Maximum-Likelihood Phylogeographic Inference The wide range of studies we discussed in this review point to BEAST as the software package of choice in terms of performing phylogeographic analyses for RABV. However, this does not mean that Bayesian inference is the only possible framework for performing such analyses. Recent years have seen the implementation of discrete phylogeographic models in different software packages that enable maximum-likelihood phylogenetic and phylogeographic inference. Both TreeTime  [90]  and PastML  [91]  allow for ancestral location reconstruction based on the same discrete phylogeographic model implemented in BEAST  [12]. One important difference between these ML applications and the BEAST implementation lies in the fact that the former both require a phylogeny to be provided as input, with TreeTime taking an unrooted phylogenetic tree as its input and PastML a rooted phylogenetic tree.
The fact that multiple applications need to be combined to perform phylogeographic analysis using ML may explain why BEAST is such a widely used software application for performing (joint) phylogeographic analyses. With increasing data set sizes as a result of genomic sequencing efforts, we do expect these ML implementations to become more widely used due to the smaller amount of time they take to obtain a location-annotated ML tree. Recent developments in fact opt for a combination of ML and Bayesian inference approaches in analysing large data sets  [92] , whereby large data sets are first analysed using ML inference followed by a more in-depth Bayesian phylogeographic analysis of specific clusters of interest. Despite these novel developments, Bayesian phylogeographic inference will remain an important staple of research studies on pathogen spread, owing to its ability to incorporate prior information, the wide range of available models and its inherent capability to take into account sources of uncertainty, to name a few.
Methods: The objective was to develop predictive survival models for patients with COVID-19 after 1-to-2 weeks in ICU. Based on the COVID-ICU cohort, which prospectively collected characteristics, management, and outcomes of critically ill patients with COVID-19. Machine learning was used to develop dynamic, clinically useful models able to predict 90-day mortality using ICU data collected on day (D) 1, D7 or D14. Results: Survival of Severely Ill COVID (SOSIC)-1, SOSIC-7, and SOSIC-14 scores were constructed with 4244, 2877, and 1349 patients, respectively, randomly assigned to development or test datasets. The three models selected 15 ICUentry variables recorded on D1, D7, or D14. Cardiovascular, renal, and pulmonary functions on prediction D7 or D14 were among the most heavily weighted inputs for both models.
Briefly, baseline information collected within the first 24 h post-ICU admission (D1) were: age, sex, body mass index (BMI), Simplified Acute Physiology Score (SAPS)-II  [8] , Sequential Organ-Failure Assessment (SOFA) score  [9] , comorbidities, clinical frailty-scale category  [10] , date of the first symptom(s) and ICU admission date. A dailyexpanded dataset included respiratory support devices (oxygen mask, high-flow nasal cannula, noninvasive ventilation, or invasive MV), arterial blood gases, standard laboratory parameters, and adjuvant therapies for ARDS until D90. In-ICU organ dysfunctions included acute kidney failure requiring renal replacement therapy, proven thromboembolic complications, confirmed ventilator-associated pneumonia (VAP) or bacterial coinfection, and cardiac arrest.
Candidate predictors We included candidate predictors considered in our previous multivariate Cox regression analyses, which assessed baseline risk factors of death by D90  [3]. D7 and D14 candidate predictors were defined a priori among data available in the COVID-ICU cohort  [3]  (i. e. , before the building of the SOSIC models), based on recent publications describing risk factors and specific complications associated with COVID-19 prognosis  [11, 12]. VAP was diagnosed by quantitative distal bronchoalveolar lavage cultures growing ≥ 10 4 CFU/mL, blind protected specimen-brush distal samples growing ≥ 10 3 CFU/mL, or endotracheal aspirates growing ≥ 10 6 CFU/mL. Pulmonary embolism was proven by pulmonary computedtomography angiography or echocardiography.
Model development We implemented a systematic machine learning-based framework to construct three mortality-prediction models (SOSIC-1, SOSIC-7, and SOSIC-14) from randomly selected development datasets, comprising 90% of the study sample; the remaining 10% were randomly assigned to the test datasets. Each prediction model was built using a gradient-boosting machine with decision trees, as implemented in the eXtreme Gradient-Boosting (XGBoost) classification algorithm  [13]. XGBoost algorithm contains several tuning parameters (e. g. , the number of decision trees, the maximal length of the component decision trees). The best set of parameters was chosen among a large grid of tuning parameters using tenfold cross-validation to maximize the prediction model's discrimination ability, as assessed by the area under the receiver operating characteristics curve (AUC).
All the steps leading to the final prediction model (including the selection of the set of XGBoost tuning parameters) were applied to every bootstrap sample  [16]. Second, model performance was assessed on the independent test datasets, distinct from development datasets used for model construction. One of the advantages of the XGBoost algorithm is its sparsity awareness that can handle the possibility of missing values  [17]. Therefore, no missing value was imputed before model development or validation. Because the COVID-19 pandemic did not hit similarly all regions, we tested the performances of the three SOSIC models in two distinct populations namely in centers from Paris-greater areas and Grand Est compared to centers from other regions. Lastly, the performances of the SOSIC-1 were also compared to the SOFA and the SAPS II scores in the development and test datasets.
Descriptive analysis Characteristics of the data included in the SOSIC scores are expressed as number (percentage) for categorical variables and means ± standard deviations or medians (interquartile ranges) for continuous variables. In a univariate analysis, categorical variables were compared with χ 2 or Fisher's exact test and continuous variables were compared with Student's t-test or Wilcoxon's ranksum test. A P value < 0. 05 was considered statistically significant. Statistical analyses and predictive model construction were computed with R v4. 0. 3, caret package v6. 0-86, and XGBoost package v1. 3. 2. 1.
Study population Among 4643 patients enrolled by May 4, 2020, 399 were lost to follow-up by D90. Thus, the predictive survival models were built based on the remaining 4244 patients with available D90 vital status. Then, 4244, 2877, and 1349 patients, respectively, were included in the development datasets to construct the SOSIC-1, SOSIC-7, and SOSIC-14 scores, with 424, 292, and 185 from each group, respectively, randomly assigned to the corresponding test datasets (Fig. 1 ). The three models selected 15 ICU (baseline) variables: i. e. , age; sex; BMI; treated hypertension; known diabetes; immunocompromised status; clinical frailty-scale category; bacterial coinfection; ventilation profile; SOFA-score respiratory, cardiovascular, and renal components; lactate concentration; and lymphocyte count).
d-Dimers were also selected a priori but were not retained for model development because of their inconsistent collection at ICU admission (Additional file 1). Selected in-ICU parameters obtained on D7 (SOSIC-7) or D14 (SOSIC-14) were: SOFA-score respiratory, cardiovascular, and renal components; lactate level, and ventilation profile. In addition, on D7 or D14, the duration of invasive MV, extubation procedure, prone-positioning, continuous neuromuscular blockade, VAP, cardiac arrest, and/or proven pulmonary embolism since ICU admission were integrated into the SOSIC-7 and SOSIC-14 scores. Table  1  reports the distributions of these variables according to D90 vital status in the D1, D7, or D14 development and test datasets. Univariate analyses of patient characteristics in the development datasets showed that those who died were significantly older and had a higher clinical frailty-scale category, lower BMI, and shorter intervals between first symptom(s) and ICU admission (except for the SOSIC-14 dataset) compared to D90 survivors (P < 0.