dataesr/scientific-paragraphs-categorization huggingface dataset:
occultans loci (with 98. 8% allele similarity with most P. occultans isolates) and 10,705 of the 13,544 P. citrophthora loci (with 77. 9% allele similarity). Based on the ITS and coxI sequencing Phytophthora clade 2 isolates TJ100, TJ101, TJ103 and TJ104 were previously designated as hybrids  (Jung et al. 2017a ). We therefore conclude that TJ093, TJ102 and TJ184 are also hybrids. The parental species of isolates TJ093, TJ100, TJ101, TJ102, and TJ103 are not in our dataset. One of the parental species of isolates TJ104 and TJ184 is Fig. 3  Average number of loci and genome size of Phytophthora species. The colour refers to the clade of the species while the shape refers to the hybrid status of the species. Isolates that deviate significantly from other isolates of their species in terms of average number of loci and/or genome size are shown separately, together with their isolate code.
DISCUSSION Our objectives were to apply genome-wide fingerprinting (i. e. GBS), supplemented with FCM, to better identify and characterize Phytophthora hybrids and to use this information to construct an improved phylogeny of the genus. We present a method that is highly reproducible, and that can be applied to a wide range of other organisms. One critical point is the availability of noncontaminated DNA. It is essential to review the data for the presence of sequence reads from non-target organisms, even when no visible contamination of the cultures is present. Contaminating sequences inflate the number of loci from the target organism, which may erroneously suggest hybrid status, and can hamper correct phylogenetic inference. Performing a BLAST search with the identified Phytophthora loci to a custom made database containing sequences of possible fungal and bacterial contaminants was an important step to prevent this artefact.
Agreement between two alternative phylogenomic methods depends on the dataset  (Dupuis et al. 2017)  and might be explained by reduced accuracy of summary methods such as ASTRAL when gene tree estimation error (GTEE) is high  (Roch and Warnow 2015; Vachaspati and Warnow 2018). The short GBS sequences can indeed lead to high GTEE  (Chou et al. 2015; Roch and Warnow 2015)  and poorly informative gene trees  (Dupuis et al. 2017; Fernández-Mazuecos et al. 2018). In that case, concatenation-based methods perform better, even when incomplete lineage sorting (ILS) is high  (Chou et al. 2015; Hosner et al. 2016; Vachaspati and Warnow 2018). Higher statistical support in concatenation-based phylogenies may, however, mask real biological reasons for low support of branches such as ILS or hybridization  (Anderson et al.
2017 ). Since we do not know the true species tree and gene trees, we also do not know the levels of ILS and GTEE in our dataset. Minimal incongruence between the two phylogenomic methods for closely related species suggests limited ILS, and we assume high GTEE because of short GBS loci. The contrasting topology of both methods was mostly associated with a different placement of clades. This is probably the result of the very small number of shared loci among highly divergent species. Indeed, reduced representation methods such as RAD-seq or GBS are especially suitable for studying closely related species  (Arbizu et al. 2016; DaCosta and Sorenson 2016; Leaché et al. 2015; Zimmer and Wen 2015). Nevertheless, we find similar interclade relationships as the ones observed in trees that were constructed based on protein data from complete genomes of 21 Phytophthora species from eight clades using Matrix representation using parsimony (MRP), gene tree parsimony (GTP) and a supermatrix of concatenated datasets  (McCarthy and Fitzpatrick 2017).
GBSseparately or in combination with other techniques such as FCMcan also be applied to related oomycete or fungal genera for species identification, to assess the presence of hybrids or to elaborate their phylogeny. Finally, our GBS data can be further explored and used to develop applications such as real-time PCR, hybridization probes, multiplex amplicon sequencing, etc. and may eventually lead to the development of (sub)clade-, species or genotype specific markers that can routinely be used for the identification of species and their hybrids.
Acknowledgements We thank  Alessandra Belisario ,  Clive Brasier ,  Jaime Aguayo ,  Johan Meffert ,  Monica Höfte ,  Svetoslav Bobev ,  Tamara Corcobado Sánchez  and  Willem Man  in 't Veld for sharing Phytophthora cultures. We thank  Mary-Ann Van Bost  for help in maintaining the Phytophthora culture collection at  ILVO , and  Cristiana Maia ,  Aneta Bačová ,  Milica Raco ,  Henrieta Bas  and  Ivan Milenkovic  for maintaining the  Phytophthora culture collection of Thomas Jung. We also thank  Ariane Staelens  for help with the GBS procedure and  Christophe Verwimp  for sharing read preprocessing scripts. Finally, we thank  Miriam Levenson  for English language editing.
Availability of data and materials The datasets generated and analysed during the current study are available in the Dryad Digital Repository, at https://doi. org/10. 5061/dryad. p40r314. All scripts that were used in the GBS data analysis are distributed under the MIT license and are available in a repository called "GBS_Phytophthora" v. 1. 0. 0, and can be downloaded from GitLab (https://gitlab. com/ahaegeman/ GBS_Phytophthora) and Zenodo with doi: https://doi. org/10. 5281/zenodo. 3363287.
, 2017). The behaviour of these models were also evaluated when multiple-HBD classes contributed to autozygosity, including comparisons when the underlying ancestors were separated by a few generations. A detailed discussion on these aspects is for instance available in  Druet and Gautier (2017)  and in the vignette from the RZooRoH package  (Bertrand et al. , 2019). Importantly, we showed that the model was robust to background Linkage Disequilibrium (LD) that was well captured by the most ancient HBD classes (i. e. , HBD segments) and thus, as desired, did not influence estimates of recent inbreeding levels  (Druet and Gautier, 2017; Solé et al. , 2017). From a practical point of view, this made LD pruning of the analyzed data set unnecessary. In addition, the multiple-HBD class model has been compared to other methods, including rule-based ROH, likelihood-based ROH and the single HBD class HMM  (Druet and Gautier, 2017; Solé et al.
e. , τ c = log ρc 1-ρc for all c ≤ L) and guarantees that all the estimated ρ c are comprised between 0 and 1 (since ρ c = exp(τc) 1+exp(τc) ). Note that it may also be possible to rely on numerical optimization with constraints instead of transforming the parameters but this is generally not recommended as the constraints might slow down convergence  (Zucchini and MacDonald, 2009). We previously tested different optimization functions and obtained the best results with the parameter transformation and optim L-BFGS-B algorithm. The N1R model is now implemented as the default model in the RZooRoH package (from version 0. 3. 1).
Application to estimation of inbreeding levels in the European bison The N1R model was tested and compared to the MixKR model on a set of 183 genotyped European bison with high inbreeding levels  (Druet et al. , 2020)  This represents a marker-density below 10 SNPs per cM but we evaluated based on simulations and wholegenome sequence data that it allowed to capture recent autozygosity  (Druet et al. , 2020). Partitioning of inbreeding levels in different HBD classes was first compared with MixKR and N1R models with five HBD classes with rates equal to {4, 8, 16, 32, 64}. In order to assess robustness of results to model specifications, we also applied models with 9 HBD classes (R c = {4, 8,. , 1024}). Analyses were carried out with the RZooRoH package  (Bertrand et al. , 2019). 3 Results.
Acknowledgements We thank the three anonymous reviewers for their valuable and constructive comments that helped us improving the original version of the manuscript. This work was supported by the  Fonds de la Recherche Scientifique-FNRS  under grants J. 0134. 16 and J. 0154. 18. Tom Druet is Senior Research Associate from the  F. R. S. -FNRS. We used the supercomputing facilities of the " Consortium d'Equipements en Calcul Intensif en Fédération Wallonie-Bruxelles " (CECI), funded by the  F. R. S. -FNRS. 0.
Data collection The ethnobotanical survey was conducted from September 2018 to February 2019 and surveyed 506 people identified by purposive selection. The choice of respondents was based on their availability and willingness to participate in the study. Approval from the ethics committee of the University of Abomey-Calavi was granted. A questionnaire was developed and took into account, among other things the characteristics of the respondents (gender, age, ethnicity. ), the main plants  used to treat small ruminants diseases and improve their productivity, the parts used (roots, bark, leaves), recipe preparation, difficulties related to plant usage, development of the formulation and degree of satisfaction (efficacy of plants). The survey was conducted in the local languages of each community and local interpreters were used if necessary.
Plant collection and identification At the end of each interview, voucher specimens of inventoried plants were obtained from interviewees, harvested from the wild, farm or garden. Herbarium specimens were mounted and identified at the National Herbarium of Benin, University of Abomey-Calavi, using the analytical flora of Benin  (Ako egninou et al. , 2006). Plant specimens were coded and deposited in the herbarium. It was not possible to obtain specimens for all plants. There were seasonal plants that were not available during the survey. We therefore deleted the data for these plants (3 plants) before data analysis.
Diversity of inventoried medicinal plants The collected data were used to establish the list of inventoried plants to treat small ruminants diseases. The number of species by genus and family was determined. To assess the diversity of plants used to treat small ruminants diseases, the Generic Coefficient (Rgc) which is the ratio number of species (Ns) over number of genera (Ng) (the inverse of the ratio defined by  Fan et al. (2017)  was determined (Equation  2 ). With Ns being the number of inventoried species, Ng the number of genera and Rgc the Generic Coefficient. If Rgc ¼ 1, then the plants used to treat small ruminants diseases have low diversity. Otherwise, each inventoried genera has only one species. If Rgc >1, there is a high generic diversity within plants used to treat small ruminants diseases.
Socioeconomic and environmental factors affecting ethnoveterinary knowledge level A matrix was constructed using socioeconomic and environmental factors (age, gender, ethnicity, agro-ecological zone, religion, household size, educational level, small ruminants herd size and the origin of the knowledge) as independent variables and the number of plants cited for the treatment of small ruminants diseases as a dependent variable. A classification analysis based on a decision tree was applied to the matrix to access socioeconomic and environmental factors which affect local knowledge to treat small ruminants diseases with plants. The analysis were carried out in R software (R Core Team, 2013), using rpartordinal package as describing by Archer (  2010 ) and the differences were considered significant at the 5% level.
The distribution areas of A. glabripennis have lower environment temperatures than that of A. chinensis  [22]. At present, there have been a few studies on the potential distribution of S. noctilio but most of their parameters were based on theoretical hypotheses rather than laboratory data  [2, 4]. We assumed that the cold tolerance and the time-space differences of S. noctilio in different populations would affect their potential distributions. We investigated the cold tolerance of S. noctilio by measured SCP of four geographic populations and combined these with LLTs. We then compared the cold tolerance of different geographical and seasonal populations. Furthermore, to examine the mechanisms affecting the temperature tolerance and distribution and, as a theoretical guide for the control of this invading pest, we discussed the capacity of S.
Historical Temperature Data and Microclimatological Measurements Historical temperature data (1981-2010) for the four sites were obtained from the National Meteorological Information Center (http://data. cma. cn). The data included extremely low temperatures, monthly average temperatures and daily average temperatures. At the sampling sites, the temperature inside the overwintering tunnels and the ambient temperature outside were measured in the stem of three Pinus sylvestris, which had been injured by S. noctilio. Data (±1 • C) were recorded every ten minutes for the whole winter by electronic thermometers (L91-1, Hangzhou Luge Technology Co. Ltd. ; Hangzhou, China). Test samples were three P. sylvestris, which were injured by S. noctilio, at the same sampling sites. We placed the temperature probes inside and outside the bored tunnel of the stem and recorded the data every ten minutes for the whole winter.
Data Analysis The SCP of the larvae were compared among different overwintering phases by analysis of variance (ANOVA) to evaluate the cold tolerance of the population as well as the changes throughout the overwintering periods. The SCP data were used as a reference for subsequent experiments. The SCP data were imported into SPSS 23. 0 (IBM, Inc. ; New York, NY, USA) and divided into three categories according to the date of collection. A least significant difference (LSD) (p = 0. 05) multiplex analysis was used to compare the differences in the SCPs of samples among different periods and the Games-Howell test (p = 0. 05) was applied for pairwise comparisons of different regions. The LSD and Games-Howell tests were the post hoc tests used with the ANOVA. We followed the methods of Li et al.
,  Feng et al. and Li et al. to analyze the SCP and MP data  [13, 20, 23]. A test of the homogeneity of variance was performed for one-way ANOVA to meet the normality criteria better and a t-test was performed between each pair of samples to obtain p-values. The relationship between the MP and the SCP was analyzed using Unpaired t-test data (after a Bonferroni-test and Bonferroni-corrected, p < 0. 05). LLT 50 and LLT 99 were calculated  [13, 18, 19]  and the latter was used to evaluate the damage to insects caused by low temperatures and the ability of the insects to tolerate the low temperature. Data analysis consisted of the Probit method implemented in regression analysis in SPSS 23. 0. October to March's historical climate data were analyzed (Graph pad prism 7, GraphPad Software, Inc. ; California, CA, USA).
11 ata from electronical data and medical reports were prospectively collected. SARS-CoV2 infection was confirmed by a positive rT-PCR on nasopharyngeal swab or bronchoalveolar lavage on admission to the authors' critical care unit. The ARDS grade was defined according to the Berlin definition. 12 he severity of illness at ICU admission was evaluated by the sepsis-related organ failure assessment score. 13 Chest computed tomography angiogram was performed prior to tracheal intubation to diagnose pulmonary embolism. TTE and TEE were performed simultaneously, for all patients in supine position, within 24 hours of tracheal intubation, by trained operators using a standardized procedure. Some parameters were assessed by TTE and others by TEE. Indeed, TTE is better for the assessment of conventional RV parameters and TEE is known to significantly underestimate TAPSE and RV-S 0 wave velocity.
Speckle-Tracking Analysis RV strain, measured by speckle-tracking echocardiography, was obtained using a dedicated software (Automated Cardiac Motion Quantification, QLAB version 9. 0, Philips Medical systems, Andover, MA). All 2D-STE measurements were performed by a cardiologist experienced in echocardiography. 2D-STE parameters were analyzed in single frame and the reported values were the average of three measurements. 2D-STE parameters were obtained by TEE in the ME 4CH view.
Tricuspid Annular Displacement Analysis For TAD analysis, three points were used for initialization on the first diastolic frame in the 2D ME 4CH view (Fig  1, A ). These points were placed (1) on the tricuspid annulus, at the insertion of the anterior tricuspid valve leaflet (RV free wall), (2) on the tricuspid annulus, at the insertion of the septal leaflet, and (3) on the RV apex. The software (Automated Cardiac Motion Quantification, QLAB version 9. 0, Philips Medical systems, Andover, MA) automatically tracked and calculated three parameters: (1) the displacement between the RV free wall and the RV apex (TAD lat ), (  2 ) the displacement between the interventricular septum and the RV apex (TAD sep ), and (3) the RV-LSF. RV-LSF was calculated as the maximum endsystolic displacement (LES) of the midannular point from the measured annular motion and is expressed in percent of the end-diastolic RV longitudinal dimension (LED): 100 £ (LED À LES)/LED).
To assess intraoperator and interoperator reproducibility for offline analysis, data of ten patients were selected randomly and analyzed by the same operator and by another operator, with an interval of at least one week between the two analyses. The reproducibility of 2D-STE measurements was evaluated using the intraclass correlation coefficient (ICC). All statistical analyses were performed with IBM SPSS software (SPSS, version 24, IBM, New York, NY). The limit of statistical significance was p < 0. 05. All p values were the results of two-tailed tests.
Results Between March 1, 2020 and April 15, 2020, 84 patients were admitted to the authors' ICU for COVID-19 infection. Among the 54 patients who required mechanical ventilation, 47 patients had moderate-to-severe ARDS and 29 patients (61%) were included (Fig  2 ). The 2D-STE parameters were feasible for the 29 patients. In 2D-strain analysis, no myocardial segments were excluded. Demographic and echocardiographic data are summarized in Table  1. Patients were categorized into two groups according to the presence or the absence of ACP diagnosed by TEE. ACP was diagnosed in 12/ 29 (41%) patients and was absent in 17/29 (59%). Age, sex, body mass index, and all ventilatory parameters were comparable (all p 0. 11) between the two groups. Before tracheal intubation, 26 Computed tomography (CTs) were performed, and pulmonary embolism was diagnosed fin two patients.
For RV dysfunction evaluation, the three-dimensional echocardiographic assessment of RV function has a better correlation with RV ejection fraction, calculated by cardiac magnetic resonance, than RV-FAC. 3 However, its routine use for bedside assessment remains very limited, especially due to specific probes availability. The left ventricle specific strain software (QLAB version 9. 0, Philips Medical systems, Andover, MA) was used for RV strain analysis, as RV specific software was not available. Nevertheless, these two methods correlate very well even if they are not totally interchangeable. 35 However, despite widespread variability in RV regional strain analysis between vendor software (GE and Philips), differences do not seem to be significant. 36 Finally, further studies are required to compare ACP and RV dysfunction prevalence according to ARDS etiology (ie, influenza, COVID-19, bacterial infection).
This IoT node is connected to a real commercial LoRaWAN standard network, including a network server and an application server for data presentation. The IoT node operates as a standard class C LoRaWAN device and is seen by the network as any other class C LoRaWAN device. Though the whole system is presented here, its different components are largely independent and could be reused with different hardware or software. Specifically, the contributions presented in this paper are as follows: 1. A multi-source energy-harvesting circuit based on off-the-shelf components that can harvest energy from a large variety of energy sources without a design change. This includes sources such as a solar panel, a low-voltage source such as a TEG (Thermo-Electric Generator), and any alternating voltage source such as a wind turbine or a piezoelectric generator.
3. An implementation based on a real-world device and LoRaWAN network instead of simulation, which corresponds to an industrial use-case. This bridges the academic results and industrial constraints. The benefits of multi-source energy harvesting are measured and demonstrated, especially when complementary energy sources are used. The rest of this paper is organized as follows. The state-of-the-art of different hardware platforms and power-management systems for IoT nodes is presented in Section 2. The hardware and software architectures of our LoRaWAN energy-harvesting IoT platform are then presented in Section 3. Section 4 details the designed energy-allocation systems for both single-task and multi-task IoT nodes. The experimental results obtained with the previously described energy-allocation policies are shown in Section 5.
Multi-Source Multi-Task Node Architecture In order to validate their design, the energy allocation policies proposed in this paper were implemented on a real multi-source EH-IoT node. The complete block diagram of the proposed node is shown in Figure  1. The hardware part is composed of two boards: a preexisting industrial LoRaWAN platform from Wi6labs and a multi-source energyharvesting board. Both the networking protocol and power management software module are implemented on a microcontroller. The use of LoRaWAN in this work serves as a context, and no modifications or contributions are made to the LoRaWAN standard stack itself. The full platform is built using off-the-shelf components, making it easier to adopt for industrial applications. The Wi6labs LoRaWAN platform is composed of a STM32 microcontroller, a SX1272 LoRa transceiver from Semtech, and their power supplies.
In state-of-the-art applications, the power management algorithm is executed at the end of fixed-duration time slots, and the computed duty cycle is applied for all transmissions during the next time slot. The duration of the time slot must be long enough to cover multiple transmissions. For example, many works use a time slot duration of one hour with mesh network communications, where the delay D TX between two transmissions varies from seconds to minutes. However, in the case of LoRaWAN communications, D TX can range from minutes to hours or even days, depending on the use case. This requires the use of very long time slots, and prevents the power management algorithm from converging to an optimized duty cycle in a reasonable time. To prevent this problem, the considered power manager is executed after each data transmission, so that the node can quickly adapt its behavior to variations in the environment.
As the solution space is very important, a fully algorithmic energy allocator will provide a suboptimal solution. There is therefore a need to determine an optimal solution to this problem. To solve this problem, we suppose the following hypotheses: -All priorities ρ i are normalized so that ∑ K i=1 ρ i = 1 -Data are transmitted only once, at the end of each time slot. This second assumption stems from the use of LoRaWAN as the transmission network. Many state-of-the-art energy management algorithms use mesh-networking technologies to transmit data, for which the delay between two messages ranges from seconds to minutes. In such a case, a time slot has a fixed duration that covers multiple transmissions. In our use case, the delay between two LoRaWAN messages can range from minutes to hours or even days in extreme cases.