dataesr/scientific-paragraphs-categorization huggingface dataset:
PCR-HRM will be useful to evaluate the prevalence of the two AcIV-E variants in Europe. In particular, it is of interest to collate data on var1 epidemiology, including its biological impact on its host. Wide screening should shed light on the origins of var1 and var2, and determine if they have both been imported into Europe via commercial trade or if they diverged in Europe from an endemic virus or from a recent imported ancestor. -.
However, in the present work, we wish to stick to the more pedestrian diagrammatic approach with external gauge fields, in which case one expands det(i / D -m) as Graphically, this series is represented by the tower of one-loop 1PI diagrams shown in Fig. 1. The main advantage of expressing the effective action in terms of 1PI diagrams is that well-tested automatic tools are available to actually compute these loop amplitudes. In the present work, we will rely on the Mathematica packages FeynArts  [38] , FeynCalc  [39] , and Package X  [40]  (as implemented through FeynHelpers  [41] ). L ef f = - 1 4 F µν F µν + i ∞ n=1 e n n T r 1 i / ∂ -m / A n. (8) For QED, all the diagrams with an odd number of photons vanish because they are odd under charge conjugation (Furry's theorem  [42] ). Let us construct the effective couplings up to order m -4.
Predicted class probability can be used to represent the classifier's confidence score as it is applied in weighted voting  [9]. However, the quality of the weighted voting depends on the performance of its base classifiers. Moreover, each supervised learning has its own method to provide a confidence score and it does not always in probability form. For these reasons, we extend the previous study on the transformation of confidence score  [15]. In the context of ensemble learning, each base learner has its own performance. The reliability score of each classifier within the binary problem is considered to handle the contrasted performances and to propose a better prediction. It is important to mention that prediction score also represents algorithm's level of certainty for each instance of a dataset, so that the composition of base classifiers can be changed dynamically based on their confidence level.
Experimentation Results and Discussion To evaluate our algorithm, a series of experiments were performed on eight different datasets. Next section discusses the dataset used, the protocol, and then results are exposed. These data are selected in order to study the behavior of our algorithm to handle from BCWD (286 instances and 10 attributes) to EES data (14980 instances and 15 attributes). The attributes vary between numeric (integer and real) and categorical. Since our focus is to study the reliability aspect of various expertise of base predictors, we avoid to use imbalanced dataset so that the performances of the algorithms are not distracted by these conditions. Class imbalance problems can be measured by the imbalance ratio (IR), defined as the ratio of the number of instances in the majority class to the number of examples in the minority class  [1].
Data Description and Protocol 1 http://www. cs. waikato. ac. nz/ml/weka/ Table  2  shows the accuracy comparison between base classifiers and ensemble methods. The ID column represents the sequence number of dataset according to the Table  1. The best algorithm is defined by an algorithm that has the highest score of accuracy and the smallest value of standard deviation. kNN shows the best result than the other learners on the side of base classifier. In another way, C4. 5 provides the smallest standard deviation among the others. Four out of five ensemble methods exceed the average scores of all single classifiers. This scenario confirms the benefit of ensemble methods to give a better accuracy score than relying on a single classifier. Three voting based algorithms (MV, WMV, DRV) show a superior average results compared to the results of Stacking, and MS.
It is normal to see that voting based methods have good results since the base classifiers scores are quite good. WMV and DRV have the same deviation score even though the accuracy score of each dataset is different. If we consider the accuracy score of ensemble methods individually, DRV provides the highest accuracy for six dataset. In contrast to the results of the first experiment, Table  3  provides a significant decreasing values on MV and WMV after 25 spammers were added. DRV gives the best result, followed by MS, Stacking, MV, and WMV respectively. MV tends to give similar accuracy score for eight dataset indicated by the smallest standard deviation score, while the accuracy scores of WMV are the lowest among the others on six data. It means that the decision of MV and WMV are distracted by the presence of the spammers, while DRV is able to select the best combination and to eliminate the weak predictions.
The ability of ensemble methods to maintain the accuracy score is illustrated in Figure  3. X-axis shows the sequence of the dataset, while Y-axis shows the absolute accuracy distance between the first and second trials (lower value is better). This score is formulated as     4a ) and after the spammers were added (see Fig. 4b ). Xaxis represents eight data used and Y-axis indicates the number of second needed in a log scale. The values written in the diagram describe the lowest and highest time in each dataset. The performances of MV and WMV were computed from the sum of training time of base classifiers, while the score of DRV was obtained from the MV and the reliability diagram building time (see Eq. 3). Due to the same complexity, MV line is not visible in the figure and is overwritten by the WMV line.
where A 1 is the accuracy value of the first experiment and A 2 is the accuracy score in the presence of spammers. The distance scores of MV are higher than DRV, Stacking, and MS on all dataset, while WMV shows the highest accuracy instability. This measure allows us to see the affect of random predictors to the popular voting techniques. On the other hand, DRV improves this drawback by considering predictor reliability aspect, indicated by the lower score similar to MS and Stacking.
where A 1 is the accuracy value of the first experiment and A 2 is the accuracy score in the presence of spammers. The distance scores of MV are higher than DRV, Stacking, and MS on all dataset, while WMV shows the highest accuracy instability. This measure allows us to see the affect of random predictors to the popular voting techniques. On the other hand, DRV improves this drawback by considering predictor reliability aspect, indicated by the lower score similar to MS and Stacking. 1000 MV WMV DRV Stacking MS 514. 522 1000 MS Stacking DRV WMV MV 620. 585 Computation time in second (logscale) 1 10 100 0. 536 1. 233 0. 262 0. 398 0. 317 0. 525 0. 454 0. 944 1. 096 9. 17 18. 132 21. 998 63. 46 70. 483 Computation time in second (logscale) 1 10 100 1. 08 1. 961 0. 305 0. 607 0. 664 0. 816 0.
Patient Population This single-center retrospective study, including chart review, was approved by our institutional review board and the requirement for written informed consent was waived. All patients admitted to our tertiary center for vascular liver disorders (Beaujon University Hospital, Clichy, France) between 2000 and 2016 with a diagnosis of primary BCS were retrieved from our prospective electronic database. Inclusion criteria were 1) the diagnosis of acute, subacute or chronic BCS, and 2) initial diagnosis and follow-up including contrastenhanced MRI. BCS was defined as hepatic venous outflow impairment at any level between the small hepatic veins and the right atrium  4. The date of the diagnosis of BCS was that of the first imaging examination demonstrating obstructed venous outflow.
For all patients, all available MRI examinations were screened by two abdominal radiologists (MVW and MR) and only those showing at least one focal liver lesion (defined as a round, well-circumscribed lesion greater than 5 mm in diameter seen on unenhanced or contrastenhanced sequences) were retained. Patients who had no focal liver lesion on initial or follow-up MRI were excluded. Patients with benign lesions only with less than 12 months of follow-up were also excluded (Figure  1 ). Demographic, clinical, laboratory, and outcome data were retrieved from patient medical records. Patients were tested for prothrombotic factors such as myeloproliferative neoplasm associated with the JAK-2 mutation, paroxysmal nocturnal hemoglobinuria, coagulation disorders, antiphospholipid syndrome, and Behçet's disease.
Final Diagnosis Lesions were diagnosed on the basis of histopathologic analysis of specimens obtained by surgical resection or percutaneous biopsy, or on the basis of clinical and biologic data combined with a minimum of 12 months follow-up. Malignant cells at histologic examination, serum AFP level greater than 250 ng/mL, or both, enabled the diagnosis of HCC. Indication for focal liver lesion biopsy were as 1) focal lesion associated with an AFP serum level >15 ng/mL; 2) lesion modification, i. e. appearance of previously absent washout, or ≥ 50% size increase in ≤ 6 months; 3) lesion > 10mm showing arterial phase hyperenhancement and washout and ancillary imaging features, especially mild-moderate signal hyperintensity on T2-weighted images or signal hyperintensity on high-b value diffusion-weighted images.
Statistical Analysis Categorical data were expressed as frequencies and percentages, and continuous variables were expressed as means and standard deviations, or medians and ranges, as suitable. A Fisher exact test or a Chi-2 test was used for comparison of frequencies. The student t test or Mann-Whitney test was used to compare continuous variables according to the distribution of data. Inter-reader agreement for washout identification was assessed with kappa coefficients (0. 00-0. 20 indicated slight agreement; 0. 21-0. 40, fair agreement; 0. 41-0. 60, moderate agreement; 0. 61-0. 80, substantial agreement; and 0. 81-1. 00, almost perfect agreement). A p-value of. 05 was considered to be statistically significant and all tests were two-sided. All analyses were performed using the Statistical Package for the Social Sciences (SPSS) software (version 23.
, 2014; Trierweiler et al. , 2014; Finch et al. , 2017) , relatively few studies have analyzed these patterns across large portions of a species' distribution  (Mandel et al. , 2011; Dodge et al. , 2014; Monti et al. , 2018). We compiled a large telemetry dataset on the Egyptian Vulture Neophron percnopterus to analyze spatial and temporal variability of migratory movements within and among individuals and subpopulations. The Egyptian Vulture is distributed across southern Europe, central and southern Asia, the Middle East, and Africa, and is listed as globally Endangered due to significant population declines caused by multiple anthropogenic threats such as poisoning, and power infrastructure collisions and electrocutions  (Botha et al. , 2017; BirdLife International, 2019; Safford et al.
Data Processing and Delineation of Migration Periods Tracking data from each project were uploaded to the online repository movebank. org (Wikelski and Kays, 2019). Erroneous GPS fixes were removed using general purpose data filters  (Douglas et al. , 2012) , with maximum plausible average speed set to 25 ms -1 and the error radius set to 30 m. To standardize the temporal resolution of the data, we censored the data to include only the first location from each individual every day. For each individual and season (fall, spring), net squared displacement (NSD) was calculated using the adehabitatLT package  (Calenge, 2006)  in R statistical software  (R Core Team, 2018). Plots of NSD over time for each individual and season were visually inspected to censor data from any seasons where an individual did not migrate (i.
e. , juveniles and immatures that stayed in Africa) or where a migration was not completed  (Bunnefeld et al. , 2011). We then calculated start and end dates of each individual migration by fitting non-linear models to plots of NSD over time, using the "disperser" model in the migrateR package  (Bunnefeld et al. , 2011; Spitz et al. , 2017; Buechley et al. , 2018b). These estimates were visually verified and manually refined. We identified the point at which an individual first initiated a migration as the first point at which NSD continuously increased away from the summer or winter range (Figure  S1 ). We defined the end of migration as the first point at which NSD values plateaued upon reaching the winter or summer range  (López-López et al. , 2014; Buechley et al. , 2018b). For all further analyses, only data from completed migration trajectories were used.
The final dataset after processing consisted of 188 complete migrations (71 spring, 117 fall; Figure  1 ) by 60 individuals (24 tagged as juveniles, 8 tagged as immatures and 28 tagged as adults). Of the completed migrations, 24 were completed by juveniles, 36 by immatures and 128 by adults, with the age at subsequent migrations adjusted according to the age at deployment (Table  1 ; Supplementary Tables  1, 2 ).
GLMMs were fitted with Gaussian distribution and identity link using the lme4 package in R  (Bates et al. , 2015) , and we considered the model with the lowest Akaike Information Criterion (AIC) as the most parsimonious and present parameter estimates from that model. Two of the migration parameters (direct distance and cumulative distance) were log transformed to meet the assumptions of GLMMs  (Zuur et al. , 2009). Models were compared using the maximum likelihood estimation and were validated by checking for homoscedasticity and normality of the residuals. To that end, relevant model diagnostic graphs were computed (residuals against fitted values, residuals against each explanatory variable, histogram of residuals and normality Q-Q plots). We computed marginal and conditional R 2 following Nakagawa and Schielzeth (2013) using the piecewiseSEM R package  (Lefcheck, 2016)  to assess the overall explanatory of the model (i.
e. , for fixed and random effects separately). All computations were performed in R version 3. 5. 1 (R Core Team, 2018). To quantify the variation in migration parameters among populations, we estimated repeatability of migration parameters as an estimate of the fraction of total variance (sum of betweenand within-population variation) that scales from 0 to 1, with 0 indicating that all the variance is within a population, and 1 indicating that all the variance is between populations  (Bell et al. , 2009; Nakagawa and Schielzeth, 2010). Repeatability was estimated with the R package rptR  (Stoffel et al. , 2017) , using the fixed factors supported by the above GLMM analysis to account for seasonal or age variation in the data. We concluded that there was significant repeatability of migration parameters within subpopulations if 95% confidence intervals of repeatability estimates did not overlap zero.
Additionally, we calculated the width of the migration corridor for each subpopulation in order to provide a measure of route flexibility  (López-López et al. , 2014). The width of the migration corridor was measured by computing the linear distance of the maximum longitudinal separation (i. e. , East-West) of individual tracks at 5 • latitude intervals from 15 • N to 40 • N, encompassing the full latitudinal range cover by both fall and spring migrations. The width of migration corridors was computed for the complete dataset of migration tracks and also for spring and fall seasons, separately. Computations were done in ArcMap 10. 0 (ESRI, 2014) using the World Latitude and Longitude 1 × 1 degree Grid (available at https://www. arcgis. com/home/item. html?id=f11bcdc5d484400fa926dcce68de3df7).
Similar to other raptors, we also found some age-related differences in migration distance, duration, speed and timing  (Sergio et al. , 2014) , with adults traveling faster along shorter routes, and departing earlier in spring, than younger birds  (Monti et al. , 2018) , although the patterns were not consistent in all subpopulations. While our dataset did not allow a full assessment of changes in individual migratory performance with age (sensu  Sergio et al. , 2014) , our findings are consistent with expectations that individual raptors must improve their migratory performance in early life to eventually be recruited into the breeding population  (Sergio et al. , 2017). Although further work is required to assess the effects of environmental factors on migratory movements of Egyptian Vultures, the variability within and among each subpopulation indicates that they could potentially respond to short term changes in environmental conditions along their flyway which could eventually affect migration phenology  (Both, 2010; Klaassen et al.
resident lifestyles  (Sanz-Aguilar et al. , 2015). With recent tagging of Egyptian Vultures within wintering ranges  (Buechley et al. , 2018a; McGrady et al. , 2018)  this may soon be possible to explore in more detail, enabling a comprehensive comparison of movement strategies in relation to human activity  (Tucker et al. , 2018). Furthermore, although our dataset did not enable the investigation of the ontogeny of migration in Egyptian Vultures [e. g. ,  Scott et al. (2014) ], future analysis of movement data derived from individuals tracked from juvenile to breeding adult status will provide a clearer understanding of the development of migration strategies and the variation within and among individuals as they age. Finally, this study illustrates that broad-scale collaboration can contribute to overcoming one of the grand challenges of migration research by enabling the mapping of flyways at a continental scale  (Bauer et al.
and Water Affairs General Directorate of Nature Conservation and National Parks and NorthStar Science and Technology for donating three transmitters each. State Nature Reserve Dagestanskiy and Russian Raptor Research and Conservation Network supported work in Dagestan. Western Europe data: deployments of transmitters in Portugal were funded by the EUfunded LIFE Rupis project (LIFE14 NAT/PT/00855); SALORO S. L. U. funded the deployment of transmitters in the Duero region of Spain; DREAL Nouvelle-Aquitaine-Fondation d'entreprises Barjane funded deployments in France; JE was supported by Basque government predoctoral grant (grant number: 569382696); GREFA (Grupo para la Rehabilitación de la Fauna Autóctona y su habitat)-Endangered Species Monitoring Project together with Poison Sentinels Project of WWF/Spain.
ACKNOWLEDGMENTS We dedicate the article to the memory of  Michele Panuccio  and his passion for and expertise in migration ecology. We also thank  Michele  and the other reviewers,  Wouter Vansteelant  and  Tom Finch , for their positive and constructive comments which improved the article. Balkans and Caucasia data: We thank our collaborators including  Kuzey Doga Society (Turkey) ,  Igdir Directorate  of  Nature Conservation and National Parks (Turkey ),  American University of Armenia, Ethiopia Wildlife Conservation Authority, Ethiopia Wildlife and Natural History Society , and our colleagues who assisted with Egyptian vulture trapping, including  Emrah Çoban ,  Lale Aktay ,  Kayahan Agirkaya ,  Berkan Demir ,  Mete Türkoglu  (Turkey);  Karen Aghababyan ,  Anush Khachatrian ,  Garo Kurginyan  (Armenia);  Sisay Seyfu ,  Alazar Daka Rufo ,  Yilma Dellelegn Abebe ,  Girma Ayalew  (Ethiopia);  Svetoslav Spasov ,  Ivaylo Angelov ,  Saniye Mumun ,  Dobromir Dobrev ,  Tsvetomira Angelova ,  Stoycho Stoychev , (Bulgaria); Ewan and  Jenny Weston ,  Emil Yordanov  who assisted with Egyptian vulture trapping in  Bulgaria Vasilis Sideris ,  Giannis Chondros ,  Christos Lambris ,  Antonis Vroikos ,  Dimitris Vavylis ,  Angelos Evangelidis  (Greece),  Mirjan Topi  (Albania),  Metodija Velevski  and  Zlatko Angeleski  (North Macedonia).
FUNDING Balkans and Caucasus data: This work was financially supported by the  LIFE+  projects  LIFE10 NAT/BG/000152  and  LIFE 16 NAT/BG/000874  funded by the  European Union  and cofunded by the  AG Leventis Foundation  and MAVA, the  US National Science Foundation , the  Christensen Fund, National Geographic Society , the  Whitley Fund for Nature ,  Faruk Yalçin Zoo  and  Kuzey Doga 's donors (in particular  Bilge Bahar ,  Devrim Celal ,  Seha Işmen ,  Lin Lougheed ,  Burak Över , and  Batubay Özkan ). We are grateful to  Turkey's Ministry of Forestry.
Kinetic properties for reverse activity by trypanosomatid PFKs An enzyme-linked assay was used to measure F6P production from the reverse reaction catalysed by PFK isoforms. Michaelis-Menten curves using allosteric sigmoidal models were generated for ADP titrations (Figure  3A ) and F16BP titrations (Figure  3B ). TbPFK is the most active isoform, with the lowest K ADP 0:5 and K F16BP 0:5 (K 0. 5 defined as the concentration of substrate at which half maximal enzyme velocity is reached, analogous to Michaelis-Menten constant [K M ]). TcPFK has similar kinetic parameters to TbPFK. LmPFK has a slightly lower V max and markedly lower affinities for both substrates, in keeping with known lower activities in the forward reaction  [17]. Full kinetic parameters are listed in Table  2. Control experiments without PFK did not produce significant quantities of ATP (data not shown).
The reverse PFK reaction can be allosterically modulated in trypanosomatid PFKs AMP is a known activator, and sole physiological allosteric effector, of the forward PFK reaction in trypanosomatids  [15]. The effect of AMP on the reverse activity of trypanosomatid PFKs was assessed using the enzymelinked kinetic assay described above. All three trypanosomatid PFKs were activated in the presence of 0. 5 mM AMP, with the k cat =K F16BP 0:5 value increasing 2-fold for TbPFK and TcPFK, and 10-fold for LmPFK (Supplementary Figures  S1  and S2 ). F26BP, a potent activator of the forward reaction in many eukaryotic Table  2  Kinetic parameters for trypanosomatid PFKs in the reverse reaction Values are mean averages (±standard error of mean; n = 3). Large standard errors for TcPFK data result from varying degree of fit of non-linear regression sigmoidal models, despite narrow error bars apparent in Figure  4.
V. Simulation Results In this part, simulation tests are conducted within a small-scale simulator in order to evaluate the impact of the proposed EM-Strategy on the behavior of the studied hybrid energy system and its performances under representative loading conditions. The system, shown in Fig. 1 , is simulated using SimPower blocks in MATLAB/Simulink. In these tests, the DC-bus voltage is controlled to 80 V while the driving load profile is emulated. The system and control parameters are listed in Table. 1. These parameters are the same then those of the experimental test bench. For each agent update its position Decrease a.
Participants We conducted a cross-sectional study in HIV-infected adults (� 18 years) treated with ART for at least 6 months attending the outpatient clinic for their one-day annual medical evaluation in two French university clinical centers located in Seine-Saint Denis department in the North of Paris (hospital 1) and in the Haut-de-Seine department in the South of Paris (hospital 2). In these centers, nearly all PLHIV in care can speak French, otherwise health care providers are used to call Interservice Migrant (ISM), a professional service for telephone translation. Between 01/05/2013 and 31/10/2014, all consecutive patients were included. Patients' medical data were retrieved from the Nadis and from the DOMEVIH electronic medical records. According to the French legislation, both records received approval from the French "Comite ćonsultatif sur le traitement de l'information en matière de recherche dans le domaine de la sante" (Registration number  15.