dataesr/scientific-paragraphs-categorization huggingface dataset:
As repeated administration of a vaccine may result in an increasingly pronounced immune response, the number of administrations in the toxicity study should exceed the number planned for human administration to ensure confidence in the safety of the dosing schedule  (Forster, 2012). Thus, we applied the "N + 1" rule, where at least one more administration is given in the toxicity study (seven injections) than in the proposed clinical scheme, which consist of successive cycles of a maximum of six injections administered at 3-week intervals (NCT01220128). The 2-week interval between the injections was aligned with the EMA and WHO guidelines for non-clinical safety assessment of vaccines and adjuvants  (EMA, 1999 (EMA, , 2005;; WHO, 2003) , and was still consistent with the 3-week interval proposed in the clinical schedule (NCT01220128).
Studies in monkeys The studies in monkeys were conducted at CiToxLAB. The cynomolgus monkey is an animal model well accepted by regulatory authorities, which was chosen because monkeys present the highest degree (>90%) of sequence homology among other species, and a similar pattern of expression with their human orthologs; thus, human tumor and self-antigens can be tested in this animal model, allowing for assessment of potential immune-and product-related toxicities. In addition, CiToxLAB maintains a historical reference database in which in vivo parameters, clinical pathology and histopathology in the cynomolgus monkey are available from all control animals used in previous studies at the laboratory, allowing for comparability regarding normal and baseline values. Furthermore, a full human dose of a given product can be injected in this species.
At the end of the treatment periods, two monkeys per group were kept for a 13-week treatment-free period to evaluate the reversibility of potential toxic effects, while the remaining four monkeys per group were killed. Studies 1 and 4 were conducted in males only as p501 + AS15 is intended to treat prostate cancer; study 3 was conducted in females only as dHER2 + AS15 is intended for the treatment of breast cancer. The number of injections was based on the "N + 1" rule mentioned above. In studies 1 and 2, monkeys received seven injections, reflecting single clinical cycles of six doses of p501 + AS15 (NCT00148928) or PRAME + AS15 (NCT01149343), administered every 2 weeks. The 20 injection schedule in studies 3 and 4 reflected the maximal human clinical exposure, with three cycles of six doses of dHER2 + AS15 (a total of 18 doses)  (Hamilton et al.
The seroconversion was calculated on sera at the dilution of 1 : 2000 for p501 and PRAME and 1 : 100 for dHER2. For WT1, the limit of quantification of the test was chosen as threshold; because the calculated threshold was below the limit of quantification, the dilution of 1 : 40 000 was used to calculate seroconversion. OD was measured using a microplate reader (Bio-Rad Laboratories, Berkeley, CA, USA) connected to a computer. Data were captured with the SoftMaxPro software and analyzed.
Study in rabbits Body weight, body temperature and clinical pathology data were analyzed using one-way analysis of covariance, with corresponding pretreatment values as covariates, using Bartlett's test and Shapiro-Wilk's test, and, for data showing non-homogeneity of group means, group t-tests or the Mann-Whitney rank test. Food consumption data were analyzed using Dunnett's multiple comparison test. The statistical analyses were performed using Provantis software (version 6. 5). Incidences of histopathological changes were analyzed using Fisher's exact probability test. Continuous and semicontinuous data were reported as arithmetic means and SD.
Acknowledgments We are grateful to  Dr. M. K. Prinsen  ( TNO Quality of Life ) and to teams in GSK Vaccines for their contribution to these studies and in particular to  S. Veenstra  for studies coordination and  M. Bisteau  for IHC analyses. We also thank  Urszula Miecielica, PhD ,  Mihai Surducan, PhD  ( XPE Pharma & Science ) and  Virginie Durbecq, PhD  ( XPE Pharma & Science , on behalf of  GSK Vaccines ) for providing medical writing services and editorial support in preparing this manuscript.
The latter was probably due to the low re-isolation frequency in brain, as it favors random fluctuations and 'founder effects' over actual selection pressure. Using microarray analysis, we were able to detect most of the barcodes of the injected strains in all infected organs (supplementary material Table  S3 ). For our analysis, we have used combined DNA of pools isolated from the same organ of three individual mice at each time-point. In order to validate this detection approach, we compared array data of selected mutants with quantitative PCR (qPCR) data obtained from the same samples isolated from the individual mice. The overall correlation between the array-based MVI and the means of the qPCR data was good (Pearson r=0. 78, Fig. 3A ) and in range of similar comparisons using expression data  (Morey et al.
The CV for each mutant (represented by a single point; same genes as for panel A) was calculated from the qPCR data of different mice. An increase in variation can be observed over time, and the brain exhibits a generally high CV at each time point. same model was generated with brain data from mice 2 days p. i. , which did not correlate well with the FVI. As expected, the coefficient was very low (-0. 2) and not statistically significant (Table  3 , Fig. 5C ). Similarly, completely randomized FVI-MVI pairs held no measurable predictive value (data not shown). Overall, the fly virulence index had the highest predictive power for mouse liver burden at day 7 p. i. Interestingly, using the in vitro growth as the basis, the prediction of the murine virulence class is not as reliable. For the total MVI (excluding the brain), a slow in vitro growth predicts a hypovirulent mutant with a maximum probability of 60%, and hypervirulence is estimated to have a 40% chance (Fig.
Remaining differences can be explained by the specific conditions encountered in the organs, ranging from overall geometry, nutrient supply and presence of organ-specific host molecules to differences in immune cell populations and cytokine profiles. The latter is known to play an important role, for example, in establishing C. albicans infection of the kidneys  (Lionakis et al. , 2013). As expected by the low reisolation counts, the brain data deviated strongly from those of the other organs. Hence, we calculated the MVI by combining data only from liver, spleen and kidney. Our results show that the overall disease progression of poolinfected mice was not different from established single-strain infection models  (Jacobsen et al. , 2010a). The pool size of approximately 40 strains was chosen to keep the total number of mice in the experiment low, while enabling us to detect larger changes in mutant abundance.
Array design and barcode detection Arrays were designed to detect the barcodes. Isolated fungal DNA was mixed with DNA standards containing additional barcodes. Barcodes were PCRamplified and labeled with cyanine dyes. DNA isolated from the plated inoculation pools and from the re-isolated strains (pooled from three mice each) were labeled with different cyanine dyes for a two-color hybridization with dye swaps to obtain two technical replicates. Array data were evaluated using GeneSpring GX, Version 12. 1 (Agilent, Santa Clara, CA). Only data with signal-to-noise ratios of >2 in the inoculum were included. The relative amount of a mutant following infection was determined by dividing the signal intensities of the mutant's barcode in the inoculum by the signal intensity in the re-isolated pool.
Statistical analyses GO term enrichment was calculated using GO::TermFinder  (Boyle et al. , 2004) , with all mutants screened in the fly as background population. Sets of genes were considered enriched if P<0. 05 and the false discovery rate Q<5%. GO terms were obtained from the Candida Genome Database (www. candidagenome. org). For correlation analyses, GraphPad Prism, version 6. 00 (GraphPad Inc. , La Jolla, CA) was used. For correlation analysis of organ means, mouse data were only used when values for >50% of organs and time points were available. Correlation values are given as Pearson correlation coefficient r. For the multinomial logistic regression models, MVI and FVI were transformed into three categorical variables (virulence class), with the reference class neutral (-0. 5<MVI/FVI<0.
5), hypovirulent (MVI/FVI< -0. 5) and hypervirulent (MVI/FVI>0. 5). Predictor variables for the mouse virulence class were FVI values or previously published in vitro datathe relative growth in YPD medium at 30°C, compared with the average growth rate of the whole collection  (Schwarzmüller et al. , 2014). For fly virulence class, log 2 converted in vitro data served as predictor variables. Models were fitted and coefficients were obtained by using R software (version 2. 15. 2) and the nnet package (version 7. 3-5).
Acknowledgements We thank  Nadja Jablonowski ,  Stefanie Allert  and  Maria Bucksch  for the technical assistance with DNA isolation and qPCRs,  Ursula Stöckel  and  Birgit Weber  for their help in the animal experiments, and  Marie-Celine Lacombe  and  Jessie Bourdeaux  for their assistance with the fly experiments. We also thank the reviewers of this manuscript for their constructive comments and suggestions.
e. , whether to promote reforestation or agriculture  (Carstensen 1958). Different groups responded dissimilarly to deforestation, which led to varying landscape feedbacks. Using a GIS, we characterized landscape composition and structure (scale: 65 ha [160 acre] cell size), focusing on the white pine-dominant component only, at four states  (1860, 1930, 1950, 1990). Study sites (N = 95) were identified via a stratified random sample of historic (1860) white pine-dominant sites, based on relative importance value (RIV). We assessed dominant land cover to quantify forest landscape change for  1860-1930, 1930-1950, and 1950-1990. Land-cover data are derived from historic forest inventories (U. S. Public Land Survey for 1860, Wisconsin Land Economic Inventory for 1930) and remotely sensed imagery (aerial photography for 1950, multi-temporal satellite imagery for 1990).
Data sources We derived environmental history data from government reports, correspondence, and newspaper articles of the National Archives (Record Group 75, BIA), the University of Wisconsin-Madison archives (University of Wisconsin Agricultural Experiment Station reports), the Wisconsin State Historical Society (e. g. , Lake Superior Farmer), and the Ashland, Wisconsin, BIA field office. Second, we conducted oral history interviews in 2002-2004 (N = 40; 11 with participants from the Indian reservation, 29 with participants from the NIPF zone). Population data were compiled from records of the U. S. Census Bureau (Census of Population and Housing, http://www. census. gov/prod/www/decennial. html). We analyzed records for the period 1890-1990 at the county scale. We limited our analysis to the primary county of the study area, Ashland County, Wisconsin (70.
Acknowledgments: We are grateful for the suggestions of three anonymous reviewers whose input significantly improved the manuscript. Our research was supported by the  USDA Forest Service , the  National Science Foundation Coupled Human and Natural Systems Program (NSF  Grant  CNH-1013296 ), the  University of New England , the  National Science Foundation   IGERT Grant   9870703  ( Human Dimensions of Social and Aquatic Systems Interactions ), a  USDA McIntire-Stennis research grant  to  N. Langston , and a grant from the  Wisconsin Department of Natural Resources Forestry and Science Services  to  D. Mladenoff. We thank the  Bad River Band of Ojibwe , especially the Bad River Natural Resources Department; the  Nature Conservancy Chequamegon Bay office ; the oral history interview participants;  T.
Acknowledgments We thank all of the members of the  French-Libyan Paleontological Mission, notably Awad A. Bilal (Benghazi) ,  Mouloud Benammi ,  Yaowalak Chaimanee  and  Olivier Chavasseau  (Poitiers),  Pauline Coster  (Kansas),  Bernard Marandat  and  Laurent Marivaux  (Montpellier). The MPFL research project is a scientific collaboration between the  University of Poitiers (France)  and the  University of Tripoli (Libya). The MPFL fieldwork and research is supported by the  Agence National pour la Recherche  (program  EVAH , number  ANR 05-BLAN-0235 ). We extend gratitude to the editor who handled the manuscript and to the anonymous reviewer and to  Alison Murray  ( Canada ) for their comments.
Introduction Selection bias, also termed dataset shift or domain adaptation in the literature  [8] , occurs when the training distribution P (x, y) and the test distribution P (x, y) are different. It is pervasive in almost all empirical studies, including Machine Learning, Statistics, Social Sciences, Economics, Bioinformatics, Biostatistics, Epidemiology, Medicine, etc. Selection bias is prevalent in many realworld machine learning problems because the common assumption in machine learning is that the training and the test data are drawn independently and identically from the same distribution. The term "domain adaptation" is used when one builds a model from some fixed source domain, but wishes to deploy it across one or more different target domains. The term "selection bias" is slightly more specific as it assumes implicitly that there exists a binary variable S that controls the selection of examples in the training set, in other words we only have access to the examples that have S = 1.
Simple step sample selection distribution In this second experiment, we consider a simple step distribution with known or estimated selection probabilities and we apply this selection scheme on a variety of UCI data sets in order to assess the efficiency of our bias correction procedure in more realistic scenarios. We use a SVM classifier for both classification and regression tasks. Experiments are repeated 100 times for each data set. In each trial, we randomly select an input feature x c to control the bias along with 300 training samples. We then apply the following single step probability distribution as discussed in Theorem 1, P (s = 1|x = x c i ) = p s = p1 = 0. 9 if x c i ≤ mean(x c ) p2 = 0. 9 1+exp(r) otherwise where r is a parameter that controls the strength of the selection bias.
1) is our noisy estimate of r. For each weighting scheme, we fit a true weighted model (denoted as P in Table  1 ) and an approximated weighted model (denoted as P in Table  1 ). As p 1 < 1 and p 2 > 1, our weighting mechanism satisfies the assumptions of Theorem 1, so we set B * = 1. We report the mean square errors (MSE) in Tab. 1. All values are normalized by the MSE of the unweighted model (our gold standard). As may be seen from the plots in Fig. 2a  and 2b , the combined models outperform the weighted ones. That is, when using either exact probability ratio, the results obtained with P mix are better than that of P. The same observation can be made when the estimated probability ratios are used instead (i. e. , Pmix versus P ) and except on the Banknote data set. The gain is significant at the significance level 5% using the Wilcoxon signed rank test.
Conclusions In this paper, we showed that the standard importance weighting approach used to reduce the bias due to covariate shift can easily be improved when misspecified Even for more general covariate shift scenarios, we proved that there always exist a threshold for the importance weight below which the test instances should be predicted by the globally biased model. In view of this result, we proposed a practical procedure to estimate this threshold and we discussed a simple procedure to combine the weighted and unweighted prediction models. The method was shown to be effective in reducing the bias on several UCI data sets.
(2003)  who found a 2 to 5 h precursor effect. A re-analysis of the SAMPEX database confirms a 4 h precursor effect  (Sgrigna et al. , 2005). Similar results have been recently found using data acquired by the NOAA-15 satellite during a period of 11 years  (Fidani and Battiston, 2008). Evidences for a lithosphere-magnetosphere coupling are reported in  Battiston and Vitale (2013) , where low energy (<0. 3MeV) electron precipitations and earthquakes (magnitude M≥5) correlations were investigated. The used data were collected with the NOAA POES satellites 15 to 18 during 13 years. The unknown earthquake electromagnetic emission (EME) capture height is a main parameter of this analysis. A range of capture heights was spanned, by associating each earthquakes to an effective L-shell parameter equal to the canonical L, at a nominal height, times (cos(l)/cos(i)) 2 , with l and i the magnetic latitude and inclination.
DEMETER is a French micro-satellite operated by CNES and devoted to the investigation of the Earth ionosphere disturbances due to seismic and volcanic activities. Launched on June 29, 2004, it was turned off on 9 December 2010, after more than 6. 5 years of scientific mission. Careful statistical studies were performed on the influence of seismic activity on the intensity of low frequency electromagnetic waves in the ionosphere, first based on the first 2. 5 years survey of electromagnetic emissions  (Němec et al. , 2008) , and then using the complete DEMETER data set  (Píša et al. 2012 (Píša et al. , 2013)). Fig. 4 (right)  shows that the normalized probabilistic intensity obtained from the night-time electric field data is below the "normal" level shortly (0 -4 hours) before the shallow (depth < 40 km) M5+ earthquakes at frequencies of about 1 -2 kHz.
Other density changes have been statistically detected at the altitude of the satellite above earthquake epicenters from a few hours up to a few days before the shocks by  He et al. (2011)  and  Li and Parrot (2012, 2013). CHAMP (CHAllenging Minisatellite Payload) is a German small satellite mission  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010)  managed by GFZ for geoscientific and atmospheric research and applications, providing highest quality data for the investigation of geomagnetic and gravity field. Balasis and Mandea (2007)  examined, for the first time, CHAMP satellite magnetic and electron density data using a wavelet analysis technique to find out electromagnetic signatures possibly related to the 26/12/2004 Sumatra megathrust (M9. 3) earthquake. The authors conclude that, due to the complexity of the ionospheric system, further investigations are required in order to answer the question of whether a series of anomalous signals ("events") found in the data can be associated with the earthquake and to assign their possible usefulness with respect to earthquake development.
Due to the relevance of the role of the atmosphere in the lithosphere-ionosphere interaction, it is greatly important in the next future to systematically study atmospheric parameter variations in the context of earthquake forecast (e. g. Daneshvar et al. , 2014). For example, air temperatures are available and can be quickly downloaded from the National Centers for Environmental Prediction (NCEP) Global reanalysis dataset, which is generated by assimilation with ground weather station and satellite data.
Geomagnetic field The Information Theory  (Shannon, 1948 ) can be applied with success to satellite geomagnetic data for extracting eventual anomalies. This technique has high time-space resolution using a preliminary wavelet analysis (e. g. see  Balasis and Mandea, 2007  for wavelets applied to satellite geomagnetic data) in order to detect shorter-wavelength anomalies. Cianchini et al. (2009)  computed the wavelet entropy content of CHAMP satellite magnetic data, taken over periods including the times of two large earthquakes occurred in the Sumatra region, revealing an anomalous period some minutes before the largest earthquake occurrence. More recently,  Cianchini et al. (2012)  have analysed the components of the geomagnetic field variations, measured at the ground based magnetic observatory of L'Aquila (Central Italy), introducing the Transfer Function Entropy.
e. the ionospheric scintillation. If received by specially modified GNSS multi-frequency multi-constellation receivers at ground, we are able to measure the ionospheric scintillation parameters associated with the TEC gradients. During the equinoctial and summer months, the ionospheric scintillation induced by EIA is a daily phenomenon in the equatorial regions. It occurs mainly in the local post sunset hours  (Spogli et al. , 2013 and references therein)  and must be taken into account when investigating ionospheric/inner magnetospheric perturbations coming from below. Fig. 11  reports the ionospheric scenario over Brazil due to the EIA derived from a GNSS network in Brazil (CIGALA/CALIBRA projects, funded by EC-FP7): on the left, the occurrence of moderate/strong amplitude scintillation (S4 index) is mapped, while on the right the corresponding standard deviation of the TEC rate of change with respect to time (ROT) is shown  (Spogli et al.
Discussion A general approach to the problem of identifying an ensemble of temporal quasi-synchronous and spatial organised anomalies, possibly associated with an impending earthquake, should be multiparametric and multi-sensor, and the investigation must be multi-disciplinary. Based on data for selected events from earthquake catalogues (time, geographical position, depth, magnitude, eventually also focal mechanism), geophysical parameters (e. g. ionospheric density, electron temperature, e. m. fields, TEC, scintillation, etc) from LEO satellites (e. g. DEMETER, CHAMP, Swarm) and ground based observations should be systematically analysed in order to detect anomalous variations. Different time intervals, e. g of about 45 days before the earthquake time and at least one week after, should be also considered to investigate different steps of the earthquake preparation phase.
g. Píša et al. , 2011; He et al. , 2014) : this latter kind of study aims at identifying the most appropriate model of lithosphere-atmosphere-ionosphere coupling. In addition, the use of the high-energy particle data collected by satellites (e. g. NOAA satellites, AGILE) will allow a deeper investigation on possible enhancements of activity (particle bursts, flux increases) geomagnetically related with lithospheric events. As stated in section 4, the impact of phenomena of anthropogenic, atmospheric and outer-space origin should be seriously taken into account when searching for the lithospheric signatures in the geospace. A best practice to remove (or avoid) outer-space contribution in the ground based as well satellite data is a proper data selection, based on periods with low geomagnetic indices (the most useful are Kp and Dst) and at particular local time, so removing most of the aperiodic and periodic magnetic variation of external origin.
These perturbations can be pre-, co-or post-seismic signals and will constitute an ensemble of sequentially time ordered, spatially organised and physically related anomalies, and their correlations with seismic cycles will be assessed. The emphasis must be put on the analysis of the measurements from the three topside ionosphere missions and Cluster can be used to separate from signals originated in the magnetosphere. Ground data from seismic and geomagnetic stations, together with ionosondes, nearby a corresponding earthquake epicenter can be used, whenever a nearby ground station exists, in order to compare them with the satellite observations. The rich datasets of  CHAMP (2000 CHAMP ( -2010) )  and  DEMETER (2004 DEMETER ( -2010) )  can be used for statistical analyses of past earthquake events, while Swarm constellation of three satellites will serve for the study of seismic events after its launch (22 November 2013), as well as the next satellite CSES (Chinese Seismo-Electromagnetic Satellite), specifically designed for finding e.