dataesr/scientific-paragraphs-categorization huggingface dataset:
The unit transportation costs ( c ik ) and prices for spoke links are calculated based on the distances between the traffic areas. The transportation time between nodes are also calculated by assuming that the regular buses (used in spoke links) have a range of 40 km/h speed and BRT buses and metro trains have 50 km/h and 70 km/h speed, respectively. Table  2  shows the input parameters used in this paper. In this table, parameters with the QMP indices are the parameters given from the QMP data and parameters with the TDS indices are the parameters available in the Turkish data set. Γ mq 1 k , Γ mq 2 k and Γ mq 3 k (N#P) show the capacity of the first, second and third levels of hubs with mode m (m = {t, b}) for a problem with N nodes and P hubs. R m kl (N#P) shows the capacity of hub link k-l with transportation mode m for a problem with N nodes and P hubs.
Acknowledgements Open Access funding provided by the  Qatar National Library. Also, the third author carried out part of this work while he was affiliated with  Sultan Qaboos University. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creat iveco mmons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Strategies that use microbial diversity as a predictor of ecosystem services and function stability rely mainly on the "unified neutral theory of biodiversity" (1), which posits that distant taxa can be functionally equivalent. However, the conclusions derived from such analyses have been limited  (2). Hence, being able to define whether some species are ecologically equivalent or not is of crucial relevance to the understanding of the role played by microbial biodiversity in ecosystem functioning, as well as for environmental monitoring and diagnostics. To address this challenge, recent initiatives aim at generating catalogues of microbial biodiversity, within specific environments (e. g. , the MiDAS Global Database for wastewater treatment systems  [3] ) or at the global scale (e. g. , the Earth Microbiome Project  [4] ), as well as related genomic content.
Thus, it is important to continuously support traditional and cutting-edge microbiological wet-lab approaches and to continue to develop databases for experimentally based gene annotation. Such a strategy will likely alleviate this identified limitation by bringing about a more extensive understanding of the functions encoded and expressed by microbial populations. We can even envisage obtaining predictions of the stability of encoded proteins as a function of pH, temperature, or oxygen concentration based on their sequence. This will give us extensive knowledge of the physicochemical range in which activities of interest are possible and, thus, a finer estimation of niche breadths. For now, however, this information is not yet "readable" in DNA sequences.
ACKNOWLEDGMENTS This work was funded by  IdEx of University of Strasbourg  as part of the investments for the future program managed by the  French National Research Agency. I thank all members of my current and past laboratories, in particular, my mentors  S. Vuilleumier  and  P. Wilmes , for exciting discussions on my topics of interest. I also express my thanks to  L. Hardion  for his fruitful thoughts on and criticism of the manuscript.
Previously reported TRPM4 variations To date, 30 TRPM4 variations associated with cardiac channelopathies have been reported in the literature (Table  2 )  (Daumy et al. , 2016; Duthoit et al. , 2012; Gualandi et al. , 2017; Hof et al. , 2017; Kruse et al. , 2009; Liu et al. 2010; Liu et al. , 2013; Stallmeyer et al. , 2012). All were in a heterozygous status: 30 missense and 1 non-sense variations. Despite functional studies for some of them, clinical consequences of these variants remain difficult to evaluate. Only eight are totally absent in public SNPs databases and a majority of them (17/30) were detected with more than 10 occurrences in gnomAD consortium. Mutation carriers were also reported into various clinical phenotypes such as right bundle branch block, atrio-ventricular block, progressive familial heart block type I, isolated cardiac conduction disease, Brugada syndrome and Long QT syndrome  (Kruse et al.
As emphasized in the article by De Meeûs et al. [7]  three main issues do bias the observed heterozygosity at microsatellites: null alleles, short allele dominance (SAD) and stuttering. These originates from poor PCR amplification. As a result, an excess of homozygosity is observed at the microsatellite loci leading to overestimation of the variation statistics FIS and FST as well as increased linage disequilibrium (LD). For null alleles, several methods and software do help to reduce the bias, and in the present study, De  Meeûs et al. [7]  propose a way to tackle issues with SAD and stuttering. The authors study a dataset consisting of 387 samples from 61 subsamples genotyped at nine loci of the species Ixodes scapularis, i. e. ticks transmitting the Lyme disease. Based on correlation methods and FST, FIS they can uncover null alleles and SAD.
Stuttering is detected by evaluating the heterozygote deficit between alleles displaying a single repeat difference. Without correction, six loci are affected by one of these amplification problems generating a large deficit of heterozygotes (measured by significant FIS and FST) remaining so after correction for the false discovery rate (FDR). These results would be classically interpreted as a strong Wahlund effect and/or selection at several loci. After correcting for null alleles, the authors apply two novel corrections: 1) a re-examination of the chromatograms reveals previously disregarded larger alleles thus decreasing SAD, and 2) pooling alleles close in size decreasing stuttering. The corrected dataset shows then a significant excess of heterozygotes as could be expected in a dioecious species with strong population structure.
The FDR correction removes then the significant excess of homozygotes and LD between pairs of loci. FST on the cured dataset is used to demonstrate the strong population structure and small effective subpopulation sizes. This is confirmed by a clustering analysis using discriminant analysis of principal components (DAPC). While based on a specific dataset of ticks from different populations sampled across the USA, the generality of the authors' approach is presented in Figure  6  in which they provide a step by step flowchart to cure microsatellite datasets from null alleles, SAD and stuttering. Several criteria based on FIS, FST and LD between loci are used as decision keys in the flowchart. An excel file is also provided as help for the curation steps. This study and the proposed methodology are thus extremely useful for all population geneticists working on non-model species with large number of samples genotyped at microsatellite markers.
The method not only allows more accurate estimates of heterozygosity but also prevents the thinning of datasets due to the removal of problematic loci. As a follow-up and extension of this work, an exhaustive simulation study could investigate the influence of these data quality issues on past demographic and population structure inference under a wide range of scenarios. This would allow to quantify the current biases in the literature and the robustness of the methodology devised by De Meeûs et al. [7]. 249-260. doi: 10. 1007/s10592-006-9146-5 [5]  Koffi, M. , De Meeûs, T. , Séré, M. ,  Bucheton, B. , Simo, G. , Njiokou, F. , Salim, B. , Kaboré, J. , MacLeod, A. , Camara, M. , Solano, P. , Belem, A. M. G. and Jamonneau, V. (2015). Population genetics and reproductive strategies of African trypanosomes: revisiting available published data.
2019-09-03 Dear Thierry (and co-authors), I thank you for answering all comments by the reviewers. Many thanks also for clarifying your position regarding issues of using other software, which has been most helpful to me. Can I ask you to add some of the info your gave as a reply to reviewer 2 and myself (as 2-3 sentences in the manuscript)? Providing this last minor revision, I would accept and write a recommendation of the preprint. I copy below some parts of your reply which could be added in the manuscript to justify the difference of your aim/results to using Structure or other software. I would personally add the DAPC figure you suggested in your reply to me, but I leave it up to you if you wish to do so. "By contrast to the aim of this study, clustering techniques are useful to detect a Wahlund effect.
2019-08-12 Dear authors, You have addressed satisfactorily most points from the first two reviewers, and I am pleased to say that the paper has gained in clarity. The Figure  6  has proven to be very effective in summarizing the steps of data curation. However, as you want to stay with the format of a short communication, the study still reads quite narrow in focus and appears as a specific problem arising from this particular dataset. The lack of generality of the study is highlighted by the new reviewer 2. This reviewer has suggestions which would require work beyond the scope of a short communication namely 1) to conduct in depth study of spatial structure/past demographic history (emphasize the biological results), or 2) perform a simulation study (emphasize the methodological results).
Aurelien Tellier Additional requirements of the managing board: As indicated in the 'How does it work?' section and in the code of conduct, please make sure that: -Data are available to readers, either in the text or through an open data repository such as Zenodo (free), Dryad (to pay) or some other institutional repository. Data must be reusable, thus metadata or accompanying text must carefully describe the data. -Details on quantitative analyses (e. g. , data treatment and statistical scripts in R, bioinformatic pipeline scripts, etc. ) and details concerning simulations (scripts, codes) are available to readers in the text, as appendices, or through an open data repository, such as Zenodo, Dryad or some other institutional repository. The scripts or codes must be carefully described so that they can be reused.
Kind regards Martin Reviewed by anonymous reviewer, 2019-08-09 18:17 De Meeûs et al. investigated the effects of common biases associated with SSR datasets, i. e. null allele, short allele dominance and stuttering, and proposed recommended steps for analyzing biased SSR dataset in general. The authors investigate this question on a total of 387 (? right) tick individuals sampled across the US and genotyped for nine SSR markers. I found the manuscript well written and the questions interesting but I still agree with the previous reviews. Indeed either the authors choose to: -make a tick-centered analysis, i. e. analyze the dataset to infer population structure, diversity, demographic history, taking into account some biases associated with their SSR dataset, and if they wish, also explaining their framework to prune and analyze it; -or either the authors choose to perform a methodological paper devoted to the analysis of 1) pseudo-observed data simulated under different assumptions (e.
g. as suggested, testing different sampling size, but may be also reproductive systems, different number of biased and unbiased markers, different extent of null alleles, short allele dominance and stuttering…), and 2) of their dataset as done here, and if possible of previously published data sets, to draw general conclusions on how to handle the biases and, if possible, providing new toolkits. Therefore, at that stage the paper either lacks a theoretical analysis of pseudoobserved unbiased and biased SSR datasets to draw general conclusions, or do not provide deep population genetic analyses to understand the specific evolutionary history of the tick species/populations in the US. If the theoretical analyses cannot be performed, population genetic structure analyses, and even demographic history of the species, should be provided to get a comprehensive view of the evolutionary history of the disease vector.
Major comments: 1) The authors repeat several times in the manuscript that their data suggest strong population subdivision. However, they do not provide analyses of population structure (e. g. with STRUCTURE and/or TESS softwares, DAPC analyses) with their biased and unbiased datasets. Such analyses should be added. Even inferences of the demographic history may be interesting to explore. 2) The authors pooled alleles close in size to correct for stuttering. They chose their filtering threshold based on the assumptions that in small dioecious species population you expect heterozygous excess, so an extra care was performed to not remove rare alleles. Again, this assumption is a tick-centered, or a dioeciouscentered, hypothesis. If the authors would like to provide a framework for a large audience, they should provide wider assumptions, for different model systems for instance, and if possible, using already existing tools.
For instance, in the same way the authors cite the FREENA software, there is the AUTOBIN macro (https://www6. bordeaux-aquitaine. inra. fr/biogeco_eng/Scientific-Production/Computer-software/Autobin). Perhaps the authors could use Autobin with different threshold to pool alleles and to provide a guideline for different model systems to correct for stuttering? This is just an idea, but at the moment I still found this pooling methodology a black box and very hard to apply in a general manner. Minor comments: -I am still confused with the number of samples, I think the author should summarize in Table  1  the total number of individuals per site (AL1, AL2, …), for instance by adding a line "TOTAL" for each site, and also adding a line TOTAL at the end of the table for the full dataset. -Figure  6 : the authors should add the associated population genetic software to use for each step.
2019-06-11 Dear authors, Both reviewers and myself do find the topic of the study and the results to be of interest and relevant. Citing reviewer 1, the quest for interpreting difficult microsatellite data indeed deserves attention. It is thus of special interest to understand the biases which can be introduced during the curation steps of these datasets. If the aims, methods and interpretations are clear, the study would benefit from two major improvements. These would enhance the generality of the paper and its relevance for the wider community. First, both reviewers point out the lack of theoretical "a priori" expectations in the paper. In comments 1-3, reviewer 1 asks to describe the rationale behind the idea that experimental artifacts should increase LD. Reviewer 2 would like to understand in a more quantitative manner the rationale behind pooling alleles close in size and the effect of the sample size on the results.
The latter is important as in the study the authors chose a small sample size, while microsatellites have bene recently applied to much larger datasets (at least on many fungal pathogen species for example). A more thorough comparison with other existing curing methods could be provided. I would suggest as a possible solution to indeed build simulated datasets and apply curing methods revealing the different experimental artifacts. It would thus be possible to reveal general rules and outcomes of applying different curing approaches (including yours), such as changes of basic statistics and LD estimates. The effect of the sample size could also be tested on the same pseudo-observed data by subsampling. This general "theoretical" set-up would allow an in depth discussion of the mechanisms involved and make the article more general in scope.
The biological dataset of the tick Ixodes scapularis analyzed here would then be used as an application of these general principles. If it is not possible to perform such theoretical analysis of the curing of pseudoobserved datasets, several in depth descriptions answering comments of both reviewers should be added to the manuscript. Second, as reviewer 1 points out (comment 4), most researchers move to other type of markers (GBS, RADseq,…) and it would be helpful to discuss if the effect of curing datasets also apply to those data. As a matter of curiosity, a focus could be on highlighting how population genetics inference combining different types of markers (SSRs, GBS, RADseq) can be affected by curing some markers but not others? Several minor points are also suggested by the reviewers and need to be addressed for the revision.
Acknowledgments: We thank the following collaborators for providing the CoPc on Au film sample:  Dr. Diana Davila Pineda  and  Prof. Dr. Raul D. Rodriguez  from  IBM Research Nanocenter Operations, IBM Research -Zurich, Switzerland , and  Tomsk Polytechnic University, Tomsk, Russia , respectively. We thank  Prof. Heiko Peisert  and  Prof. Thomas Chassé  ( Eberhard Karls University of Tübingen, Germany ) for helpful discussion regarding the molecular orientation. We thank  M. S. C. Achim Junginger  ( Eberhard Karls University of Tübingen, Germany ) for the assistance in calibrating the spectrometer. We made use of the program WSXM for illustrating the optical and topography images [66]. Financial supports through  CSC Scholarship ,  DFG  ( ME1600/5-3 ), and  European COST Action   MP1302  Nanospectroscopy are acknowledged.
baumannii IC I and IC II strains from the genomes available in the databases (Supplementary Figure  S6 ). The sequences of Wzc and PglC proteins of non-clonal A. baumannii strain ATCC 17978 and A. venetianus strain Rag-1, for which CPS profiles have been determined, were also included. The IC II strains and non-clonal ATCC 17978 strain formed a separate cluster regarding both proteins, whereas A. venetianus strain Rag-1 tend to group with the IC I cluster. The observed clustering correlated with identified CSP profiles of IC II and IC I strains with the same capsule type as aligned A. baumannii strains from the databases as well as with reported CSP profiles for ATCC 17978 and A. venetianus strain Rag-1. This indicates that distinct CPS profiles could possibly be triggered by the genetic differences in capsule-related genes between two clones.
Introduction Nintedanib slows disease progression in patients with idiopathic pulmonary fibrosis (IPF) by reducing the rate of decline in forced vital capacity, with an adverse event profile that is manageable for most patients. We used data from six clinical trials to characterise the safety and tolerability profile of nintedanib and to investigate its effects on survival. Methods Data from patients treated with ≥1 dose of nintedanib 150 mg two times per day or placebo in the 52-week TOMORROW trial and/or its open-label extension; the two 52-week INPULSIS trials and/or their open-label extension, INPULSIS-ON; and a Phase IIIb trial with a placebo-controlled period of ≥6 months followed by open-label nintedanib were pooled. All adverse events, irrespective of causality, were included in descriptive analyses.
Methods study design The TOMORROW trial (NCT00514683) began with a 52-week period in which patients with IPF, FVC≥50% predicted and diffusing capacity of the lung for carbon monoxide (DLco) 30%-79% predicted were randomised to receive placebo or one of four doses of nintedanib (50 mg once daily, 50 mg two times per day, 100 mg two times per day, 150 mg two times per day). 7 Patients who completed 52 weeks' treatment in this period (period 1) continued treatment in a blinded phase (period 2), until the last patient had completed 52 weeks' treatment in period 1. This was followed by an open-label extension period in which all patients received nintedanib (NCT01170065). 8 In each of the INPULSIS trials (NCT01335464 and NCT01335477), patients with IPF, FVC≥50% predicted and DLco 30%-79% predicted were randomised 3:2 to receive nintedanib 150 mg two times per day or placebo for 52 weeks.
9 Patients who completed the 52-week treatment period and a follow-up visit 4 weeks later were eligible to receive open-label nintedanib in INPULSIS-ON (NCT01619085). 10  In the Phase IIIb trial (NCT01979952), patients with IPF, FVC≥50% predicted and DLco 30%-79% predicted were originally randomised to receive nintedanib 150 mg two times per day or placebo double-blind for 12 months, but this was amended to a placebo-controlled period of 6 months following regulatory approval of nintedanib in some participating countries. The double-blind period was ≥6 months for some patients due to the variable time required to implement the protocol amendment. 11 In all these trials, dose reductions from 150 to 100 mg two times per day and treatment interruptions were allowed to manage adverse events. No dose re-escalations were permitted in the TOMORROW trial, but dose could be increased from 100 to 150 mg two times per day in the open-label extension of the TOMORROW trial, and in the other trials described above.
safety and tolerability All adverse events reported by investigators, irrespective of causality, were included in this analysis. Adverse events were coded according to preferred terms in the Medical Dictionary for Regulatory Activities (MedDRA) version 19. 1. Safety data from INPULSIS-ON and the Phase IIIb trial were based on a snapshot conducted on 15 April 2017. The other trials had been completed by this date, but the MedDRA coding was updated to obtain consistent coding within the pooled population. Based on the mechanism of action of nintedanib, bleeding, major adverse cardiovascular events (MACE), MI and hepatic enzyme elevations were considered of particular interest. Bleeding was based on the standardised MedDRA query (SMQ) 'haemorrhage terms (excluding laboratory terms)'. MACE were based on fatal adverse events in the MedDRA system organ classes 'cardiac disorders' and 'vascular disorders'; fatal and non-fatal events in the subordinate SMQ 'myocardial infarction'; stroke based on selected preferred terms from the subordinate SMQs 'haemorrhagic cerebrovascular conditions' and 'ischaemic cerebrovascular conditions'; and the MedDRA preferred terms 'sudden death', 'cardiac death' and 'sudden cardiac death'.
MI was based on the subordinate SMQ 'myocardial infarction (narrow definition)'. Hepatic enzyme elevations were based on a list of MedDRA preferred terms. Event rates per 100 patient exposure-years were calculated based on adverse events with onset after the first dose and up to 28 days (or 14 days in the TOMORROW Weibull  13  and exponential  14  parametric survival distributions were fit to pooled Kaplan-Meier survival data for patients treated with nintedanib 150 mg two times per day and placebo and extrapolated to estimate life expectancy over the long term. The extent to which these statistical models fit the data was estimated using Akaike's information criterion. The estimated survival of patients treated with nintedanib and placebo was calculated from the area under the curve. Survival time was estimated from randomisation in the double-blind placebo-controlled trials and from the first intake of nintedanib if started in the open-label trials.
Acknowledgements Medical writing assistance, supported financially by  Boehringer Ingelheim , was provided by  Julie Fleming  and  Wendy Morris  of  FleishmanHillard Fishburn, London, UK , during the preparation of this manuscript. The authors received no direct compensation for the development of the manuscript, were fully responsible for all content and editorial decisions, were involved at all stages of manuscript development and have approved the final version. Boehringer Ingelheim was given the opportunity to review the manuscript for medical and scientific accuracy as well as intellectual property considerations. contributors All authors were involved in planning the analyses, in the interpretation of data and in drafting the manuscript. Funding The trials that provided data for these analyses were funded by  Boehringer Ingelheim.