dataesr/scientific-paragraphs-categorization huggingface dataset:
g. ,  Hebert et al. 2003; Blaxter 2004; Quast et al. 2013) , albeit with a few limitations. Identifying what level of molecular difference defines a species is also a challenging task, as it refers to the debated plethora of species concepts and ultimately relates to underlying speciation mechanisms  (Roux et al. 2016). Nonetheless, several methods for species delimitation using DNA barcoding data have been proposed  (Pons et al. 2006; Puillandre et al. 2012; Zhang et al. 2013). Moreover, the addition of novel data has in some cases challenged previous species delimitations, highlighting the need for automated and time-proficient algorithms to delimit species  (Ratnasingham & Hebert 2013) , ideally relying on the analysis of multiple loci in a coalescent framework  (Yang & Rannala 2014). Despite the apparent simplicity of using DNA sequences to build a Library of Life, the number of species collected in the core database is far from representative of the diversity on Earth.
For instance, on the GenBank taxonomy page (Anonymous 2017), it is stated that all the organisms in the public sequence databases currently represent no more than 10% of the total number of described species. In the context of the on-going 6 th global biodiversity crisis (mostly referred to as a mass extinction), the major challenge that taxonomists are currently facing is to describe species diversity before it actually becomes extinct. Whereas some taxa and some regions on Earth have been thoroughly studied (i. e. , more than 70% of plants are presumed to have been described already), the biodiversity of certain communities (e. g. , soil) and some of the smallest organisms (e. g. , microbial eukaryotes (de  Vargas et al. 2015) , fungi  (Hawksworth 2001 ) and small inconspicuous animals  (Appeltans et al.
Perspectives toward a comprehensive DNA Library of Life High-throughput DNA sequencing has widened the field of possibilities for automated and accelerated taxon identification  (Coissac et al. 2016). This technological advancement has enhanced the use of molecular data to study functional ecology, organism interactions, community ecology and biodiversity, plus the dynamics and evolution of past and present interactions. Nevertheless, further efforts are needed to aggregate a comprehensive DNA Library of Life. The well-known taxonomic impediment is of course relevant, even if some authors have questioned it  (Appeltans et al. 2012). In addition, one of the major current challenges consists of improving our sampling methods and instrumentation to discover organisms that live in extreme environments.
Acknowledgments We would like to express our sincere thanks to  Koen Martens  for accepting to host this special set of papers in the European Journal of Taxonomy. We are indebted to  Charlotte Thionois  and  Danny Eibye-Jacobsen  who took care of most of the production process. The entire editorial team of European Journal of Taxonomy has always been most helpful. This special set of papers would not have been possible without the encouragement, the scientific support and the financial contributions of the  Institut Ecologie et Environnement (CNRS) , the  Muséum national d'Histoire naturelle , and the  Institut national de Recherche agronomique. The Sanger sequencing has been performed with the operational support of the  Commissariat à l'Energie Atomique at Genoscope. This is publication ISEM 2017-014 of the  Institut des Sciences de l'Evolution.
We optimized a workflow to describe and follow the immunoinflammatory status of 550 patients (septic shock, severe trauma/burn and major surgery) during the first 2 months after their initial injury. On each time point, two immune functional tests will be performed to determine whole-blood TNF-α production in response to ex vivo lipopolysaccharide stimulation and the T lymphocyte proliferation in response to phytohaemagglutinin. In addition, a complete immunophenotyping using flow cytometry including monocyte HLA-DR expression and lymphocyte subsets will be obtained. New markers (ie, levels of expression of host mRNA and viral reactivation) will be also evaluated. Reference intervals will be determined from a cohort of 150 age-matched healthy volunteers. This clinical study will provide, for the first time, data describing the immune status of severe ICU patients over time.
sampling schedule Samples and clinical data will be collected 3-4 times within the first week (early time points) with the aim to evaluate the modulation of the immune status early after injury. Samples will be collected at day 1 (the morning following injury), at day 2 (for the severe trauma group) and at day 3/4 and day 5/7 (table  1 ). Samples will also be collected before surgery, at day 0, as surgical patients are the only group for which sampling can be performed before injury. Additional samples will be collected during late time points to evaluate the recovery of the immune status, at day 14 (between day 13 and 18), day 28 (between day 26 and 36) and day 60 (between day 52 and 68), depending on patient availability and technical constraints (figure  1 ). Total volume of sampling will be 30 mL at each time point.
sample size and data analysis plan Population sizing The number of healthy volunteers required to determine the reference intervals for the two immune reference tests was defined according to the methodology recommended by the Clinical and Laboratory Standards Institute C28-A3 guidelines. 28 The minimal number of subjects recommended being 120, after exclusion of aberrant values (CI of 90%), we decided to include 150 healthy volunteers to take into account exclusions related to technical reasons, aberrant values or consent withdrawal. For this reference population, the age range of healthy volunteers group has been carefully calculated to include the expected age range and gender distribution from ICU patients in France (table  1 ). The main objective being descriptive, the computation of the sample size was based on secondary objectives, especially for (1) the analysis of the occurrence of immunosuppression, its depth and impact on innate and adaptive immune responses (Cohen's d is 0.
Clinical data For each patient, an electronic case report form including socio-demographic, clinical and para-clinical information will be completed by clinical research assistants (table  2 ): adescription of the hospital stay, the documentation on the type of injury (surgery, burn, trauma or septic shock) and the severity as defined by the ASA classification, SOFA score  30  and SAPSII score. 30 In addition, we will collect routine laboratory results about the CMV, HSV1 serology and complete blood count. Moreover, we will document if there is any specific treatments administered to the patient, such as antibiotics, exposure to invasive medical devices and secondary infections. All data will be transferred to a TranSMART 30 database following curation for data exploration and analysis.
The proposed approaches then perform independently classification on every single frame. In order to associate the lack of temporal information, we update labels through a Markov chain by observing their transitions between consecutive frames. From point-of-view of the UAV technology, designing an UAV data transmission is a critical task which consists of various factors such as mechanical, electrical, data rate restriction, control loop delays, inter-operability, and inter-changeability  (Fahlstrom & Gleason, 2001). The main functions of an UAV data transmission are an up-link that allows the ground station to control the UAV, and a down-link that provides transmitting UAV status (or telemetry) and sensor data to the ground. In this study, the goals of the designing UAV data transmission are bounded for the down-link transmissions, in which we focus on minimal requirements of the bandwidth to send imagery to the ground station.
The reasons are that frames still need to be refresh 30 times per second to get smooth observation according to the human eyes. Furthermore, the advantages of frame rate reduction also help to improve processing time of the algorithm. The recorded images do not have to wait in the queue too long time and it reduces the computation time. The spatial image resolution reduces UAV data transmission base on a mapping between original image pixels and new image pixels. For example, an original image with the resolution of 512 × 512 may be reduced to 256 × 256 by mapping a block of four pixels into one pixels using averaging operator of values in that block. This affects to the quality of the feature descriptors by the information loss of the image.
Experimental datasets The material for evaluating consists of six video sequences captured by a consumergraded camera attached in an UAV device. The videos are captured in various contexts such as urban, countryside, and mountains. These videos have been captured with a spatial resolution of 2298 × 1294 pixels. The length of each images sequence is totally 1 m 22 s. The image sequences are captured at 30 fps. For the training data, 66 frames are randomly selected from six videos. They have been labelled by hand. To evaluate performance with standard configuration, we select key frames in testing videos. Such key frames are extracted by uniform sampling from the original videos. The result and the mask are compared pixel by pixel. If an area is marked as black, it is ignored in the evaluation.
Histological study After anesthesia of mice, hearts were excised and immediately placed in a 10% neutral-buffered formalin at room temperature for 24 hours after a brief rinse with PBS. The specimens were embedded in paraffin, cut in 5-μm sections, and stained with hematoxylin and eosin. Cardiomyocyte cross-sectional areas were calculated on a digital microscope (×400) with ImageJ (version 1. 34S) software. Masson's trichrome staining was used to detect fibrosis in heart sections and fibrotic areas were also calculated on a digital microscope (×400) with ImageJ (version 1. 34S) software.
But, as the community of Caenorhabditis evolutionary biologists has grown-now sufficiently large to merit regular meetings and dedicated stock and databases (Supplemental Material, Table  S1  in File S1; Carvalho et al. 2006; Haag et al. 2007; Braendle and Teotónio 2015)-so too has the array of evolutionary problems being investigated with experiments (Table  1  lists some of the studies that will be covered here). Caenorhabditis are free-living bactivorous roundworms with over 25 species currently being cultured in the laboratory  (Kiontke et al. 2011; Felix et al. 2014) , although only C. elegans, C. briggsae, and C. remanei have been utilized in EE research. A distinctive feature of this group of nematodes is that facultative selfing evolved independently from ancestral obligatory outcrossing three times  (Kiontke and Fitch 2005).
It is usually assumed that control populations are under no selection, but it is perhaps more realistic to think that they are under stabilizing selection for intermediate trait values. In mutation accumulation (MA) experiments, since selection efficiency is low, a domestication stage prior to subjecting populations to new environmental conditions is not necessary. Any differentiation among replicates within each treatment is presumed to result from genetic drift and idiosyncratic selection (e. g. , placement within incubators, the experimenter doing the culturing, etc. ). We illustrate divergence and differentiation of a quantitative trait with individual-based simulations (http://datadryad. org/, doi: 10. 5061/dryad. bg08n). The plots in the figure show these simulations of divergence and differentiation for 20 populations reproducing exclusively by selfing evolving under genetic drift and no selection (gray lines), or genetic drift and selection (black lines).
2009; McGrath et al. 2011 ; Here, we consider how the number of replicate populations and sample size affect the power to detect responses to selection on a quantitative trait when reproduction occurs exclusively by selfing. We rely on the numerical simulation model presented in http://datadryad. org/, doi: 10. 5061/dryad. bg08n; the reader can explore it to generate trait value trajectories and power curves as a function of standing and mutational genetic variance, population size and number of offspring, truncation and Gaussian selection, sample sizes, etc. The analysis we show is based on the simulated evolutionary trajectories of Figure  1. Individual fitness was defined as:  1 , right plot), with the new fixed phenotypic optimum set to 1 SD from the ancestral phenotypic distribution, and an intensity resulting in an initial linear selection gradient of 0.
In mutation accumulation (MA) experiments, since selection efficiency is low, a domestication stage prior to subjecting populations to new environmental conditions is not necessary. Any differentiation among replicates within each treatment is presumed to result from genetic drift and idiosyncratic selection (e. g. , placement within incubators, the experimenter doing the culturing, etc. ). We illustrate divergence and differentiation of a quantitative trait with individual-based simulations (http://datadryad. org/, doi: 10. 5061/dryad. bg08n). The plots in the figure show these simulations of divergence and differentiation for 20 populations reproducing exclusively by selfing evolving under genetic drift and no selection (gray lines), or genetic drift and selection (black lines). With lower initial heritability it takes longer for selection to take populations to the new phenotypic optimum (scenario 1; left), and the higher the initial heritability the faster the initial response (scenario 2; middle).
Figure2Detecting phenotypic differentiation and divergence. The statistical power to detect divergence or differentiation depends on many variables. Here, we consider how the number of replicate populations and sample size affect the power to detect responses to selection on a quantitative trait when reproduction occurs exclusively by selfing. We rely on the numerical simulation model presented in http://datadryad. org/, doi: 10. 5061/dryad. bg08n; the reader can explore it to generate trait value trajectories and power curves as a function of standing and mutational genetic variance, population size and number of offspring, truncation and Gaussian selection, sample sizes, etc. The analysis we show is based on the simulated evolutionary trajectories of Figure1. Individual fitness was defined as: w = exp{2[offspring_trait_value 2 optimum]^2/[2*(intensity^2)]} (Figure1, right plot), with the new fixed phenotypic optimum set to 1 SD from the ancestral phenotypic distribution, and an intensity resulting in an initial linear selection gradient of 0.
Methods This prospective observational study had a repeated measures design and was conducted in accordance with the Declaration of Helsinki (ethical principles for medical research involving human subjects) and international guidelines for the conduction and reporting of clinical investigations  [International Organization for Standardization, 2011; World Medical Association, 2013]. Ethics committee approval for study participation was obtained for each participating clinic as required at the national or local level. The outline of the Cochlear IROS and the list of approving ethics committees are available for public access (ClinicalTrials #NCT2004353, https://clinicaltrials. gov). Formal written informed consent was obtained from each recipient prior to their enrolment into this study. Clinics were advised to approach patients in chronological order, following their decision to implant any hearing device from the company Cochlear Ltd.
HUI3 and SSQ self-assessments were completed by the recipient either on paper (usually in the clinic or at home) or directly online depending their choice upon consent to participate. Data entry was performed from the completed paper forms by a representative of the clinic. By study design, and through the use of autoprompts for online data entry, 100% completion of the HUI3 and at least 45 out of 49 questions for the SSQ were requested as required for analysis.
Subject Inclusion Any routinely treated hearing-impaired adult or adolescent (>10 years old), meeting local criteria for hearing implant treatment with any hearing implant from the company Cochlear was eligible for enrolment ( Fig. 1  ). Only data for enrolled unilateral CI recipients were included in this study. At online enrolment, a subject study code was automatically assigned to each recipient, thus maintaining their full anonymity on the study platform. Patient demographics including medical and hearing histories were collected both via the clinician from the patient hospital file and via the recipient directly at enrolment using the respective case report forms ( Table  1  ). However, responses to individual demographic questions on these case report forms were not compulsory, thus allowing for missing responses.
Statistical Analysis The aim of the data analysis reported in this paper is to address the following questions: 1. How do outcomes for HUI3 and SSQ scores change over time for the unilateral CI cohort? 2. What is the nature of the missing data at follow-up intervals and the impact on outcomes? 3. Are preimplantation patient variables associated with longitudinal outcomes for SSQ and HUI3? Analysis of all of the available data extracted from the central database, the Cochlear IROS, on October 30, 2015, was undertaken using R version 3. 2. 0 [R Core  Team, 2015]. Statistical analysis was performed by a coauthor (consultant statistician at Macquarie University, Sydney, Australia). For description of the data, a summary of continuous variables using means and SD or SE and of categorical variables using numbers per subgroup and percentage of the total study cohort was created.
Longitudinal Performance Outcomes The distribution of outcome scores for the available data at each time point for each outcome measure is presented in Figure  2. Median scores suggest an increase from baseline and stability from 1-year postimplantation onwards. At each time point a wide range of scores were observed. Estimated mean outcome scores and SE are shown for each outcome measure at each assessment interval in Table  2. Analysis of change over time suggests a highly statistically significant difference in group scores between at least one pair of assessments for all outcome measures ( p < 0. 001). Tukey pairwise tests ( Table  4  ) indicated highly significant improvements from the baseline interval (i. e. , preimplantation status) to the first and second year postimplantation, respectively, for the HUI3 multiattribute utility scores.
Acknowledgements The coauthors would like to sincerely acknowledge the contributions of the following academics and experts at each clinic for their support in working with the implant recipients for counsel-ing for their study participation, performing evaluations, and data capture. In alphabetical order by implantation city or clinic:  Andrzej Sieśkiewicz  and  Marek Rogowski  from  Bialystok, Poland ;  Andreas Buechner ,  Anke Lesinski-Schiedat ,  Hannes Maier ,  Eugen Kludt , and  Angelika Illg  from  Hannover, Germany ;  Rozario Lezaun  from  Pamplona, Spain ;  Bożena Wiskirska-Woźnica ,  Alicja Sekula , and  Magdalena Kałos  from Poznan, Poland;  Maria del Prado Venegas Pizarro  and  Julia de Juan Beltran  from  San Sebastian, Spain ;  Jennifer Perold ,  Gill Kerr ,  Suryn Lombaard ,  Deidré Stroebel , and  Surida Booysen  from Tygerberg, South Africa;  Juan Royo Lopez  from  Zaragoza, Spain ; and  Sara de Torres Iera  and  Lara Roman Delgado  from  GAES (Spain).
Patient Selection A global, multicenter network of thoracic oncologists accrued patients with RET-rearranged lung cancers to this registry. 5] [6] [7]  Eligible patients had a pathologic diagnosis of NSCLC of any stage (I to IV) and RET rearrangement by a validated test that was performed in an accredited local laboratory. Accepted test methods were fluorescence in situ hybridization, reverse transcriptase polymerase chain reaction, and nextgeneration sequencing. Validation of test results by a second method was not mandatory. Investigators administered multikinase inhibitors cabozantinib, vandetanib, sunitinib, sorafenib, alectinib, lenvatinib, nintedanib, ponatinib, and regorafenib according to the approved initial starting dose of these drugs in their respective approved cancer indications-data on dose interruption and modification were not collected.
Data Collection and Response Assessment Anonymized clinical data-age, gender, RET upstream fusion partner, tumor stage, date of diagnosis, initiation and completion of RET inhibitor therapy, progression, and death-were recorded. Anonymous data were collected centrally at the University of Toulouse. The registry was opened in June 2015 and data cutoff was on April 15, 2016. Patients who were treated with a RET inhibitor outside of the context of a clinical trial were eligible for analysis of efficacy of RET inhibitor therapy. 5] [36] [37]  Best response to systemic therapies, defined as a complete or partial response achieved at least once during the course of therapy, was assessed locally by each investigator using Response Evaluation Criteria in Solid Tumors (RECIST v1. 1). 38 As a result of the limits of this registry and the lack of a formal response assessment plan for each patient, response confirmation could not be assessed and overall response rate could not be calculated.
Statistical Methods Data were summarized according to frequency and percentage for qualitative variables as well as by medians and ranges for quantitative variables. Comparisons between groups were performed by using the x 2 test or Fisher's exact test for qualitative variable test, and by the Mann-Whitney test for quantitative variables. Progression-free survival was measured as the time from the first administration of RET inhibitor therapy to progression defined by RECIST v1. 1 or death from any cause. Patients who were alive without having experienced progression at the time of analysis were censored at their last follow-up. Overall survival was measured as the time from the first administration of RET inhibitor therapy to death from any cause. Patients who were alive at the time of analysis were censored at their last follow-up.
DISCUSSION To the best of our knowledge, GLORY represents the largest single database of patients with RET-rearranged lung cancers. This global, multicenter registry was organized independently of industry support, and with limited academic resources, generated meaningful clinical data within a short time period. The number of contributions and participating centers exceeded our expectations, which demonstrated the interest of the community in less common driver mutations as well as the feasibility of international academic (11%) 1 Sorafenib (n = 2) 0 0 2 0 0 0 Alectinib (n = 2) 0 0 0 2 0 0 Lenvatinib (n = 2) 0 1 0 1 0 0 Nintedanib (n = 2) 1 0 1 0 0 0 Ponatinib (n = 2) 0 0 2 0 0 0 Regorafenib (n = 1) 0 0 0 1 0 0 NOTE. The best response to a multikinase inhibitor with activity against RET is summarized for 53 patients with advanced RET-rearranged lung cancers.
However, Tregs subsets dynamics and the particular role played by each subset during chronic infection are still unclear. Targeting Tregs subsets to shift the balance between tolerance and immunity in the clinic remains challenging. To this end, the use of recombinant interleukin 2 (rIL-2) has been beneficial as several successes of low-dose rIL-2 therapy in animal models of autoimmune pathology  [10] [11] [12] [13] [14]  and human clinical studies in hepatitis C virus induced vasculitis, chronic graft-versus-host disease (GVHD), Type1 Diabetes (T1D), systemic lupus erythematosus (SLE), and Alopecia areata  [15] [16] [17] [18] [19] [20] [21]  have been reported. These successes led to other clinical trial studies, including rheumatoid arthritis, ankylosing spondylitis, psoriasis, Behcet's disease, Crohn's disease and ulcerative colitis (TRANSREG, clinicalTrials.