dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses Continuous variables were summarized by means ± SD and categorical variables by frequencies. Associations between daily BP (systolic or diastolic) and daily steps counts averaged by month were assessed using linear mixed models, which handle repeated data for each participant. We used quadratic regression to present graphic associations between BP and steps per day at the first month (which represent baseline, cross-sectional associations) (STATA 13; StataCorp LP, College Station, TX). For the longitudinal analyses, we used data from the validated months (available BP measurement and step count as previously defined). The difference between mean values of BP (systolic or diastolic) for the validated months was used as the outcome variable. Similarly, the difference between mean values of the number of daily steps for each validated month was used as the independent variable.
Sensitivity analyses demonstrated an interaction effect of physical activity and BMI on BP; thus, analyses were stratified according to weight status (normal weight, overweight and obesity). In all models, covariates included age, sex, number of days with recorded steps, and number of monthly BP measurements (per month and per day). Inclusion of time of day for the BP measurements as a covariate in the models did not appreciably alter the findings; thus, it was not included as a covariate in the model. For all analyses, the significance level was set at 0. 05 and all tests were two-tailed. Nongraphical analyses were performed using SAS software (version 9. 4, SAS Institute Inc. , Cary, NC, USA).
Characteristics of the study population Subjects were mostly middle-aged (age range 19-90 years, mean between 50. 7 and 52. 6 years old across BMI group), with the large majority being men (Table  2 ). Subjects had validated data of WPAT and WWBPM for a mean of 4 months. BP was measured 2 times per day on average for days with at least one measurement. The obese population had a shorter follow-up, had less steps at baseline, and a higher SBP and DBP than other groups.
Sensitivity results The associations between step counts and BP in the overweight and obese groups were also observed using data with at least 15 and 20 days of physical activity tracker and BP measurements (Supplementary file 1). Additionally, in these analyses, an increase of more than Abbreviations: BP, blood pressure; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; SD, standard deviation. P for trend or χ 2 were all <0. 0001, except for the number of BP measurement per month (P = 0. 80) and number of BP measurement per day (P = 0. 01). a For days with at least one blood pressure measurement. 3000 steps per day was also found to be associated with a decrease of systolic BP in the normal weight group (-0. 95 mm Hg, P = 0. 01 and -0. 89 mm Hg, P = 0. 04 for 15 and 20 days/measurements, respectively).
Observational data The primary observational data set used in this study was collected by AWSs at selected locations on Vatnajökull. Since 1994, 1-13 stations have been operated on the ice cap during the summer months (e. g. Oerlemans et al. , 1999; Guðmundsson et al. , 2006). The temperature, relative humidity, wind speed, and wind direction at 2 m above the surface have been measured during the entire period (1992-present), while the radiation components have been measured since 1996. For this study, data from five AWSs were considered -three on Brúarjökull (B) and two on Tungnaárjökull (T) (see Fig. 1 ). The uncertainties of the AWS observations vary depending on the sensor. The temperature and humidity sensors have an accuracy of 0. 2 K and 2 % for temperature and humidity, respectively, while the accuracy of the wind speed is 0.
3-5. 0 µm) from the MODerate Resolution Imaging Spectroradiometer (MODIS) were used to create a background map of the ice albedo at all glacier grid points in HIRHAM5, which was used in the implemented HIRHAM5 albedo scheme. MODIS product MCD43A3 v006 was used for the background map  (Schaaf, 2015). The MODIS estimates of the albedo on Vatnajökull are in good agreement with AWS data  (Gascoin et al. , 2017). The MODIS data were extracted in geographical coordinates (long-lat) at a resolution of 0. 005 • , i. e. close to the original MODIS resolution of 500 m. This was done using the MODIS reprojection tool with the bilinear interpolation method. These MODIS data in latitude-longitude coordinates were then resampled to match the rotated HIRHAM5 long-lat grid coordinates by bilinear interpolation using MATLAB's interpn function  (MATLAB, 2015).
In order to determine the bare ice albedo at each grid point, daily MODIS data over Iceland from the period 2001-2012 were used. Years with volcanic eruptions were discarded, as the volcanic ash lowered the albedo values far below the average. The minimum autumn albedo value was then determined in each grid point using values from July-September and that value used to create a bare ice albedo map of the glaciers. The final albedo map had ice albedo values in the range 0. 03-0. 3 for Vatnajökull. The spectral properties of ice in the ablation zone are controlled by tephra layers in the ice, which are exposed as the glacier melts  (Larsen et al. , 1996). Additional tephra or dust deposition will therefore only have a small effect on the spectral properties of the ice, as the ice surface is already covered in dark bands.
Validation method AWS data from the period 2001-2014 for three Brúarjökull stations and two Tungnaárjökull stations are considered, as well as SMB point measurements from 1995 to 2014. All stations were operated during the summer months, but since 2006 the lowest Brúarjökull station has been operated year round. Comparisons are made between daily averages from the HIRHAM5 model and the in situ observations collected at the AWSs. HIRHAM5 daily means are calculated from 6hourly outputs, while the AWS daily means are calculated from observations at 10 min intervals. Comparisons between station values and model values are made by bilinearly interpolating the model output to the measurement position using the four closest model grid points and using only glacier-surface type grid cells. In order to remove the effect of seasonally varying magnitudes of the energy balance components, the percentage errors listed in Tables  2 3 4 are calculated as the root mean square error (RMSE) divided by the observations.
An underestimation of the temperature in the atmosphere could also be causing the underestimation. Figure  3b  shows the comparison of the modelled and measured outgoing LW radiation. There is a small overestimation at the T AC station, and a small underestimation of the other four stations, but in general the model reproduces the daily values well (r ∼ 0. 76). The average percentage deviation between the modelled and measured values is only around 3 %, combined with between 0. 5 and 2 % of the HIRHAM5 data points having deviations larger than 10 %. Due to an underestimation of the incoming LW radiation, and only small negative or positive biases in the outgoing LW, the net LW (incoming-outgoing) radiation has a mean negative bias at all AWS locations (-7. 9 W m -2 ).
At all measurement sites SMB is also measured at 25-120 non-AWS sites, depending on the year (Fig. 1 ). In order to estimate how well the model represents the SMB at non-AWS sites, the data from all the sites between 1995 and 2014 were compared with the HIRHAM5 simulation (Fig. 8 ; Table  4 ). The winter balance at all measured points is slightly overestimated by HIRHAM5 on average. However, this is mostly due to a large difference between measured and simulated SMB at the ice-covered, high-elevation, central volcano Öraefajökull (the white dots in Fig. 8 ). Only one site has been measured on this glacier for a few years only  (Guðmundsson, 2000) , in a spot that always receives a large amount of precipitation. However, since HIRHAM5 consistently overestimates the accumulation by 100-200 %, this one point has a large effect on the mean error.
Comparison with constant ice albedo simulation In order to quantify the changes in the model performance resulting from the new albedo scheme used in this study, which utilizes an albedo map based on MODIS data  (Gascoin et al. , 2017) , the results are compared to those of a previous run using a constant ice albedo of 0. 3. The average difference in albedo and mass balance over the period 2001-2014 in each grid point are shown in Fig. 12 , as well as the position of the AWS stations. There is little to no difference between the two runs in the accumulation zone, due to the year-round snow cover. In the ablation zone, however, using the MODIS ice albedo map has a large effect on the simulated albedo. The largest differences are found on the southern outlet glacier Skeiðarárjökull, which is unfortunately a glacier where no mass balance or AWS measurements have been conducted.
Whereas the modelled outgoing LW radiation component is within the uncertainty of the LW observations at the five stations, which is consistent with the ability of the model to capture surface temperatures, there was a larger difference between the modelled and measured outgoing SW radiation. This is partly due to the underestimation of the incoming SW radiation and partly due to inaccuracies in the simulated albedo. The albedo was simulated using an iterative, temperature-based albedo scheme (Nielsen-Englyst, 2015) with a bare ice albedo determined from MODIS data  (Gascoin et al. , 2017). The simulated albedo was generally overestimated during the summer and did not reach the lowest yearly value as early in the year as the measured albedo, particularly in the ablation zone. This was attributed to an overestimation of the snow cover in the ablation zone, an overestimation in the MODIS ice albedo compared with AWS observations, and the fact that the model does not account for the effect of volcanic dust deposition during eruptions and dust events on the albedo.
1 m w. e. That the model catches the changes in the specific mass balance well over the mass balance measurement period, and also captures the shift in mass balance in the mid-1990s, gives us confidence that the model estimates the specific mass balance of Vatnajökull well over the entire simulated period from 1980 to 2014. HIRHAM5 is therefore a useful tool to expand the time series of the specific SMB beyond the measurement years. However, as ERA-Interim reanalysis data only go back to 1979, the model would need to be forced at the lateral, for example, by output of a general circulation model. However, using other reanalysis data probably leads to different errors; this needs further investigation. The model could also be a useful tool to estimate the future evolution of the SMB of the ice cap, but this would also require a different forcing at the lateral boundary like general circulation model output.
Acknowledgements. This work is supported by project  SAMAR , funded by the  Icelandic Research Fund (RANNIS , Grant no. 140920-051 ), as well as the  National Power Company of Iceland (Landsvirkjun). Measurements from automatic weather stations and in situ mass balance surveys are from joint projects of the National Power Company and the Glaciology group at the  Institute of Earth Sciences, University of Iceland. Edited by: Xavier Fettweis Reviewed by: two anonymous referees.
5% (wt/vol) maltodextrin or 0. 5% (wt/vol) maltodextrin and 0. 5% (wt/vol) glucose. The isolated RNA was spectrophotometrically quantified, and 2 g of the total RNA was subsequently utilized for reverse transcription with the QuantiTect reverse transcription kit (Qiagen). qRT-PCR was subsequently carried out with SYBR green fluorescence using the Cfx96 real-time PCR machine (Bio-Rad). The primers used for the qRT-PCR experiments are EF1344R and EF1344L for the mdxF gene and EF1345R and EF1345L for the mdxE gene (Table  3 ). Raw data were converted into expression data by the absolute quantification method using the standard curve and then normalized using the housekeeping 5S RNA gene as a reference. The mean values and standard deviations were calculated from at least 3 independent experiments.
In order to determine whether there was a significant difference between two groups of mice, we used the Mann-Whitney test in the GraphPad Prism software (GraphPad Software, San Diego, CA). A P value of Ͻ0. 05 was considered to be significant. All experiments were carried out twice. fellowship from the Algerian government, and V. S. B. , G. D. R. , and C. M. are Career Investigators of CONICET (Argentina). We declare no conflicts of interest.
ACKNOWLEDGMENTS We thank  Maria Fernandez  for providing plasmid pAGEnt and  Benoit Haelewyn  ( CURB, University of Caen ) for expert technical assistance for the mouse infection model. This work was supported by grants from the  MinCyt/ECOS-Sud program  (action no. A09B03 ) (to C. M. and J. D. ), the  Agencia Nacional de Promoción y Tecnológica, Argentina (AN-PCyT ; contracts  2014-1513  [to C. M. ] and  2014-3482  [to V. S. B. ]), the  Intramural Research Program  of the  NIDCR, National Institutes of Health, Department of Health and Human Services ,  Bethesda, MD  (to A. P. and J. T. ), and the  Initiative d'Excellence program  from the  French government  (grant  DYNAMO ,  ANR-11-LABX-0011 ) (to J. D. ) and by a chair of excellence at the  University of Caen  for C. M. financed by the  Region of Normandy  and the  European Regional Development Fund (ERDF).
Costs associated with reproduction are widely known to play a role in the evolution of reproductive tactics with consequences to population and eco-evolutionary dynamics. Evaluating these costs as they pertain to species in the wild remains an important goal of evolutionary ecology. Individual heterogeneity, including differences in individual quality (i. e. , among-individual differences in traits associated with survival and reproduction) or state, and variation in environmental and social conditions can modulate the costs of reproduction; however, few studies have considered effects of these factors simultaneously. Taking advantage of a detailed, long-term dataset for a population of feral horses (Sable Island, Nova Scotia, Canada), we address the question of how intrinsic (quality, age), environmental (winter severity, location), and social conditions (group size, composition, sex ratio, density) influence the costs of reproduction on subsequent reproduction.
| Population monitoring and life history Population and life-history data collection began with a partial census in the summer of 2007. From 2008 onward, systematic yearly ground censuses were performed during the mid-late breeding season (July to September). Daily walking censuses focused on one of seven sections, allowing complete coverage of the island in 1 week. The location of bands (i. e. , breeding groups), bachelor groups, and lone individuals were recorded to within 5 m using a handheld Global Positioning System (GPS). At every encounter, multiple photographs were taken in addition to recording each horse's coloration, facial features, distinguishing marks, sex, age category (foal, yearling, 2-3 years old, or older), reproductive status, and group membership. Identifications were later confirmed in the laboratory using a photographic database.
Sampling was performed under From 2008 to 2015, more than 33,000 locations of horses were collected, and the likelihood of missing a horse if it was alive was very low (resighting probability across year: 0. 994 for females and 0. 992 for males). Hence, if a horse was not observed during an entire field season it was considered dead. Using birth records and morphology we were able to accurately attribute birth year for individuals born after 2007 or first observed as to age females first observed as adult accurately (n = 60) was a weakness of the data; however, in order to limit any biases resulting from underestimating age at death, we ignored females that died within 2 years after first being observed as an adult (n = 7). We retained for analyses only sexually mature females (3+ years old) with data available (as adult) for at least two successive years.
Consequently, all females included in the analyses lived to at least 4 years of age. Further, to allow including longevity, age at last breeding, and success at last breeding event in a metric of quality, we only retained females that died over the study period  (between 2007 and 2015)  and were known to have reproduced at least once. By doing so we acknowledge that females dying old may have died at an underestimated age compared to females dying young; however, whereas reduced variability in longevity may limit our ability to detect an effect of quality it should not lead to false pattern. In total, our dataset contained 496 observations (births with foals surviving to end of summer, or not) from 113 females (4. 39 ± 1. 83 [( x ± SD]). Probability of giving birth to a foal (that successfully lived to September 1) and probability of weaning (survival to age 1) was assessed by observing females during our field season, which occurred during the mid-late breeding season (July-August).
| Body condition We assessed body condition of horses for inclusion into a metric of individual quality. Condition was assessed from photographs using the index of  Carroll and Huntington (1988; see Debeffe et al. , 2016  for details). The score reflects the amount of subcutaneous fat deposition on the hips, ribs, and spine, and ranges from zero (very poor condition) to five (obese). From an analysis using the dataset considered herein (not shown), we knew that body condition of adult females did not significantly change over a field season. Mean lifetime body condition of adult females was calculated as the average body condition obtained in different years (number of years with available body condition scores per female: ( x ± SD = 4. 38 ± 1. 84, min = 1, max = 7, n = 113).
| Individual quality To determine if the approach of  Hamel, Cote et al. (2009) ,  Hamel, Gaillard et al. (2009)  could be used to obtain a multivariate metric of quality in adult females, we tested if most of the covariation among four life-history traits expected to be linked to lifetime fitness was captured by a single axis using a principal component analysis  (PCA)  with the R (R Development Core Team, 2010) ade4 package  (Dray & Dufour, 2007). These traits were longevity, mean adult body condition, success at last breeding before death [three modalities: no reproduction code 0, reproduction but offspring died before the next summer code 1, and reproduction with offspring that survived code 2] and age at last reproduction). Longevity is often associated with performance in other life-history traits  (Nussey, Kruuk, Donald, Fowlie, & Clutton-Brock, 2006)  and is a commonly used trait to assess individual quality  (Moyes et al.
| Data analysis We tested for reproductive costs and their determinants using univariate generalized linear mixed models implemented in the R package lme4  (Bates, Maechler, Bolker, & Walker, 2014). Specifically, we modeled a female's probability of giving birth (n observations = 496, We fitted the global models described above as well as all simpler models in R using the AICcmodavg package  (Mazerolle, 2015). The best models were then selected using the Akaike Information Criterion corrected for small sample size (AIC c ), which reflects the best compromise between model precision and accuracy  (Burnham & Anderson, 2002; Symonds & Moussalli, 2011). According to the rule of parsimony, we selected the simplest model within 2. 0 AIC c of the top model  (Burnham & Anderson, 2002). We also calculated evidence ratio (ER) n and AIC c weights (AIC c Wts) as a measure of the likelihood that a given model was the best among the set of fitted models.
ACKNOWLEDGMENTS We thank the numerous students, research assistants and volunteers who have contributed to the Sable Island horse project over the years, especially  S. Simpson ,  S. A. Medill ,  A. Contasti ,  K. Lysak ,  E. Richard ,  E. Tissier , and  J. Weisgerber. In-kind and logistical support was provided by  Fisheries and Oceans Canada (DFO) ,  Canada Coast Guard , the  Bedford Institute of Oceanography (DFO Science) ,  Environment Canada ,  Parks Canada Agency ,  Maritime Air Charters Limited , and  Sable Island Station (Meteorological Service of Canada ). Funding was provided by the L. David Dubé  and  Heather Ryan Veterinary Health and Research Fund , a  Royal Society International Exchange grant , the  Natural Sciences and Engineering Research Council of Canada (NSERC   Discovery Grant  to PDM, no.
Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational auto encoders (VAEs) and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel python library ChemTS that explores the chemical space by combining Monte Carlo tree search (MCTS) and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github. com/tsudalab/ChemTS.
Optimization in a too large candidate space can be inhibitively slow. In this paper, we present a novel python library ChemTS to offer material scientists a versatile tool of de novo molecular design. The space of SMILES strings is represented as a search tree where the i-th level corresponds to the i-th symbol. A path from the root to a terminal node corresponds to a complete SMILES string. Initially, only the root node exists and the search tree is gradually generated by Monte Carlo tree search (MCTS)  [15]. MCTS is a randomized best-first search method that showed exceptional performance in computer Go  [16]. Recently, it has been successfully applied to alloy design  [17]. MCTS constructs only a shallow tree and downstream paths are generated by a rollout procedure. In ChemTS, an RNN trained by a large database of SMILES strings is used as the rollout procedure.
Method ChemTS requires a database of SMILES strings and a reward function r(S) where S = {s 1 ,. , s T } is an input SMILES string. Our definition of SMILES strings contains the following symbols representing atoms, bonds, ring numbers and branches: s t ∈ {C, c, o, O, N, F, [C@@H], n, -, S,Cl, [O-],[C@H], [NH+],[C@], s, Br, [nH], [NH3+], [NH2+], [C@@], [N+], [nH+], [S@], [N-], [n+],[S@@].
, [S-], I, [n-], P, [OH+],[NH-], [P@@H], [P@@], [PH2], [P@], [P+], [S+],[o+], [CH2-], [CH-], [SH+], [O+], [s+], [PH+], [PH], [S@@+], /,=, #, 1,2,3,4,5,6,7,8,9,(, ),}. In addition, we have a terminal symbol $. The reward function involves first principle or semi-empirical calculations and describes the quality of the molecule described by S. If S does not correspond to a valid molecule, r(S) is set to an exceptionally small value. We employ rdkit (www. rdkit. org) to check if S is valid or not. Before starting the search, an RNN is trained by the database and we obtain the conditional probability P (s t |s 1 ,. , s t-1 ) as a result. The architecture of our RNN is similar to that in  [13]  and will be detailed in Section 2. 1. MCTS creates a search tree, where each node corresponds to one symbol.
Denote by x it the one-hot coded vector of the tth symbol in the i-th training string. The parameters in the network θ is trained to minimize the following loss function, min θ N i=1 T -1 t=1 D(x it+1 , P (y t )), where D denotes the relative entropy. Our RNN was implemented using Keras library (github. com/fchollet/keras), and trained with ADAM  [18]  using a batch size of 256. After the training is finished, one can compute P (y t ) from s 1 ,. , s t-1. It allows us to perform rollout by sampling the next symbol repeatedly.