dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors acknowledge support from the  Vannevar Bush Faculty Fellowship program  sponsored by the  Basic Research Office of the Assistant Secretary of Defense for Research and Engineering  and funded by the  Office of Naval Research  through grant  N00014-16-1-2502. We thank the  university of Chicago research Computing Center  for an award of computer time required for the GMT simulations. We thank the  University of Chicago NSF-MRSEC  ( DMR-0820054 ) for central facilities support. We also thank  Dr. Tian-Song Deng  for his help in the characterization experiments of the Ag nanoparticles.
Animals were run for three days to determine their ability to learn a new maze to the same criterion of less than one error per lap. Electrophysiological Recording Electrode data were recorded at a sampling rate of 32 kHz using Neuralynx Digital Lynx system (Neuralynx, Bozeman, MT). Hippocampal CA1 spikes were detected in real-time from 600 Hz to 6 kHz filtered continuously sampled tetrode data using amplitude threshold crossing. LC spikes were detected in the same manner, but with a 300 Hz to 6 kHz filter. Tetrode placement in CA1 of the hippocampus was confirmed by moveable tetrode depth, as well as by waveform shape, frequency and audio-converted sound of pyramidal neural spikes. Tetrode placement in the LC was confirmed using calculated moveable tetrode depth, LC firing frequency i. e.
QUANTIFICATION AND STATISTICAL ANALYSIS All statistical tests were conducted in Prism 7 analytical software (GraphPad). All data sets were first tested for normality using Shapiro-Wilk normality test as recommended  [55]. Data set normality was calculated without performing any transform (e. g. log10, or z-score) on the data set. Outliers were removed using ROUTs outlier test in Prism 7 with Q=1%. Statistical tests including ANOVA, Pearson correlations, t-tests, and their non-parametric equivalents were performed in a similar matter to previous research. Post-hoc analysis to adjust for multiple comparisons was used in accordance with Prism 7 statistical guide. Alpha was set to 0. 05 for all analyses and was always calculated in a two-tail manner. Graphical representations of data were created using Graphpad Prism 7 and MATLAB.
Behavioral Task Performance Animals' total errors on the familiar and altered task divided by 15 laps to show the average number of errors per lap and analyzed using repeated measures two-way ANOVA (Sidak post-hoc) for performance within a group across days and two-way ANOVA (Tukey post-hoc) for comparing between groups. For the difference between map errors and procedural errors the total number of each type of error was analyzed rather than error per lap. A repeated measures two-way ANOVA (Tukey post-hoc) was used to analyze within each error type and a two-way ANOVA (Sidak post-hoc) was used to compare both types of errors. Power Spectral Band Analysis Down-sampled raw LFP from intervals of each scored state were entered into Neuroexplorer 5 software (Nex Technologies, Madison, AL). LFP data underwent a Fast-Fourier transform (FFT) using a window with a Hann taper.
Sleep Sleep Spindle and Ripple Identification Sleep spindles were identified automatically from the entire sleep record. Automatic spindle identification was performed according to Eschenko et al 2006  [34]. Briefly, the sigma frequency (10-15 Hz) was filtered in the CA1 LFP data down-sampled to 200 Hz, taking the root-means-square (RMS) over a 100 msec window, then smoothing it with a moving average. Spindles were counted from periods at least 0. 3 s in length where the RMS exceeded three times the standard deviation of the RMS mean of all NREM sleep intervals. Reduction in NREM sleep spindle occurrence from baseline to 2 Hz stimulation was analyzed via paired t-test in Figure  1. The percent change in spindle occurrence between baseline and day 1 was analyzed via Mann Whitney Test in Figure  6.
Ripples were automatically identified similar to the methodology described in Girardeau et al 2009  [56]  using a custom script written in MATLAB. Using the RMS of the 100-200 Hz bandpass filtered LFP, ripples were identified when the RMS crossed an upper threshold of five times the standard deviation of the average RMS for NREM and IS sleep. Ripple edges were detected when the RMS fell below a second threshold of two times the RMS standard deviation. Ripples were considered only in segments corresponding to NREM sleep states. Ripple occurrence and ripple length between groups and days was analyzed with a two-way ANOVA (Sidak post hoc) whereas percent change in ripple length and occurrence from baseline was analyzed via Mann Whitney Test. Ripple-Spindle Coupling Spindles and ripples were automatically identified (see previous section).
Using custom MATLAB scripts, peri-event time histograms (PETHs) were generated using the start time of ripples as the event, and the start time of the spindles as the as the corresponding response. A range of 2500 msec prior to and following ripple onset was used, and bins were 50 msec in width. The number of spindle start times per bin was then divided by the total number of events (ripple start times) to convert to normalized probability. To test that this ripple-spindle coupling was nonrandom, shuffled ripple start time data (n=15 per animal per day) was generated using a Monte Carlo method similar to Siapas 1998  [2]  (we note this is a method of shuffle can be liberal). Shuffling was performed only during periods of NREM sleep to prevent an artificial reduction in the shuffled correlations.
Spindle probability between ChR2-and ChR2+ rats, as well as either group vs shuffled data was analyzed using via two-way ANOVA (Sidak post hoc). Single Unit Isolation Spike data were sorted into individual neurons using Offline Sorter x64 V3 (Plexon). For each tetrode recording of spikes, the data was manually sorted into single units using spike features of each of the four channels of the tetrode (e. g. principle component analysis or peak amplitude of each channel). LC call and CA1 pyramidal cell firing rate by state was analyzed using a repeated measures two-way ANOVA with Sidak post hoc. Place Cell Identification and Analysis Following unit isolation, CA1 units were first separated into pyramidal cells and fast spiking interneurons based upon firing rate while the animal was running on the maze, as well as by spike width (pyramidal cells having a wider spike than fast-spiking interneurons).
Interneuron data was then discarded from future analysis. Position tracking data from the rats running on the octagonal tract was separated into laps for the familiar and altered task. The track was then broken into 3 cm bins and linearized. Pyramidal cell spiking data was then used to generate raw rate maps and occupancy maps. Periods of less than 5 cm/s velocity were removed. Information content was calculated as in  Skaggs et al. 1993 [58]. Cells with less than an information content of 0. 15 were removed similar to Fuhs et al  [60]  and all cells that were not active a minimum of five laps on a maze were also discarded. A Gaussian smoothing filter (SD=4) was then applied to the firing rate data. Cells were determined to be place cells if their smoothed firing rate crossed threshold (1 Hz) for 3 or more consecutive 3 cm bins, in addition to meeting the above criteria.
Fecundity, fertility and survival After ingestion of the first blood meal and digestion, groups of 30 fed females were placed into cages with moist cotton and filter paper for oviposition. After the second blood meal and digestion, all females were individually placed in paper cups. The bottom of the cup was covered with moist cotton and filter paper to allow oviposition. Eggs were collected on the moist cotton. Oviposition rate was measured in individual females, and the number of eggs laid by grouped or individual females was counted using Egg Counter  [30]  software. Eggs were then grouped by treatment and put into demineralized water. The number of adult descendants of each batch was counted to determine the number of offspring produced. The number of dead females was recorded every day to ascertain the survival.
Blood-feeding Logistic regression by generalized linear mixed-effects model (glmer, binomial distribution, logit-link, lme4 package  [32] ) was used to compare the proportion of blood-fed females after the first blood meal, with batch included as a random effect. The effects of exposure to DEET during the first and second blood meal on the blood-feeding success during the second exposure were also evaluated by glmer with binomial distribution. The nature of the first and second exposure was included as fixed effect, and replicate was coded as a random factor. Post-hoc comparisons between the four groups of treatment at the second exposure were performed using multiple comparisons (Tukey's tests, multcomp package  [33] ). The effect of mother exposure on the success of blood-feeding of the offspring was analyzed by generalized linear model (glm, binomial distribution) with mothers and daughters exposure coded as a fixed factor.
Life-history traits In a first analysis, we tested for the effect of the first exposure on subsequent life history traits (type 2 ANOVA, car package  [34] ). Quantity of excreted haematin and fertility (mean number of descendants produced) were analyzed using a linear mixed-effects model with a Gaussian distribution, after sqrt-transformation and confirmation of data normality (lmer and lme4 packages). Number of eggs laid (expressed as a mean per female) was also analyzed using lmer with a Gaussian distribution, without data transformation. Emergence rate (proportion of eggs that reached adult stage) was assessed by using glmer with binomial error distribution. For all these models, replicate was coded as a random factor. In a second analysis, we tested for the effect of the first and the second exposure on each life history trait considered subsequent to the second exposure (type 2 and 3 ANOVA, car package).
Quantity of excreted haematin, fertility and emergence rate were analyzed using the same methodology as described above. Oviposition rate was analyzed by glmer with binomial error distribution. Number of eggs laid subsequent to the second blood meal was obtained from individually-followed females and analyzed by generalized linear mixed models using AD Model Builder to account for over-dispersion (glmmadmb, negative binomial distribution, glmmADMB package  [35] ). Survival was evaluated with a mixed effects Cox proportional hazards regression model (packages survival, coxme  [36, 37] ). For these models, the nature of the first and the second exposure was included as a fixed factor and replicate was coded as a random factor. Flight activity was estimated by glmmADMB with the nature of the first and the second exposure coded as a fixed factor.
The influence of several explanatory variables was investigated by including in the models: mosquito age, replicates, and the type of exposure (grouped vs individual). The contribution of each explanatory variable was assessed sequentially using ANOVA function, with non-significant terms removed from the model. Model selection was performed using AIC and analysis of the residuals (plotresid, RVAideMemoire package  [38] ). Results are presented as mean ± standard error (SE) and proportion ± 95% confidence interval (CI).
Effect of DEET exposure on mosquito life history traits after the second blood meal Data related to life history traits after the second blood meal was collected in blood-fed females followed individually across eight replicates. Activity was assessed in a subset of 203 females during blood-feeding. The size of the blood meal, oviposition rate and survival were measured in 320 females. Fecundity was estimated in the 228 females that oviposited. For fertility and emergence rate measurement, larvae were grouped by treatment so data is expressed as a mean per female. A significant effect of the first exposure was observed on several life history traits ensuing the second blood meal (Fig. 4 ). Compared to control females, DEET-pre-exposed females showed a reduced volume of blood intake (χ 2 = 9.
Acknowledgements The authors would like to acknowledge the technical/research platform dedicated on vectors at the  IRD centre  for providing mosquitoes and technical support. This platform is a member of the  Vectopole Sud network and of the LabEX CEMEB  ( Centre Méditerranéen de l'Environnement et de la Biodiversité ) in Montpellier. We also thank the  CEFE 's platform of chemical analyses in ecology (PACE) for technical assistance in chemical analyses. We are very grateful to  Angélique Porciani  and  David Carrasco  for their help in statistical analysis and their useful comments on the manuscript.
Funding This study was supported by  The French National Research Program for Environmental and Occupational Health of Anses  ( EST-2016/1/39 ), by the  Languedoc-Roussillon (LR) / Occitanie region and  EU-FEDER  under the name " Chercheur(se)s d'Avenir ", and by the  LabEx CeMEB , under an  ANR  " Investissements d'avenir" program  ( ANR-10-LABX-04-01 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Case report A 62-year-old woman with low back pain that appeared six weeks before was admitted to the emergency department. After a radiograph of the lumbar spine, osteoarthritic lesions were evoked. In the absence of improvement with analgesics, she was addressed in rheumatology. On admission, the clinical examination revealed spinal pain on palpation at L4-L5 and weight loss of three kilos. A trans-esophageal ultrasound was done and did not reveal endocarditis. Laboratory data included a blood leukocyte count of 6. 5 Â 10 9 /L, with 45% polymorphonuclear leukocytes, and a Creactive protein level of 86 mg/L. Disc biopsies and disc washing liquids samples were then collected and sent to the bacterial laboratory. After seven days of culture at 37 C on chocolate agar plates, small grey colonies resulted on pure culture.
Therefore, the ability of hydrologists to accurately measure processes and state variables is a priority for improving the understanding of processes. This fact has been confirmed by a recent community survey among 336 hydrologists  (Blume et al. 2017). According to this study, the participants thought that new measurement techniques and equipment, as well as more field measurements and monitoring, were needed to advance the hydrological sciences rather than new modelling approaches; furthermore, they saw the greatest challenges and difficulties in the maintenance of monitoring networks. While the community opinions highlighted the importance of field measurements and monitoring, 66% of the participants observed that there is a general tendency of more data mining and modelling projects and less experimental research  (Sidle 2006 , Burt and McDonnell 2015 , Vidon 2015 , Blume et al.
2012)  for models, the increased use of satellite data has raised rather than lowered the demand for ground-based observations  (Hut 2013). This is due to the low spatial coverage of current operational ground sensors  (Moradkhani 2008)  and the need for satellite data to be constantly calibrated using ground observation points. Many such ground-based observations often mandate ad hoc designed and developed sensors. Remarkably, the hydrological community has rarely felt the need for large-scale cooperation, such as is needed for large instruments, such as telescopes, research vessels or satellites. For instance, of the many scientific satellites that provide geophysical observations, only a few have hydrology as their primary mission objective (such as SMOS: Soil Moisture and Ocean Salinity; SMAP: Soil Moisture Active-Passive; GPM: Global Precipitation Measurement).
The WG also aims to provide an updated database of innovative observational approaches and a shared platform with experts from other fields. In our vision, hydrologists could find solutions to their experimental questions just by taking a look at the MOXXI website 2  and contacting members of the working group with similar interests. This paper was originally conceived at the First Topical MOXXI Conference in November 2016, and aims to report on the many research activities that revolve around the establishment of the MOXXI WG; to state the major challenges faced by the MOXXI community; and to provide guidance on future research directions.
In fact, implementing a large number of inaccurate observations may result in better areal rainfall estimation than only using a couple of accurate raingauges  (Haberlandt and Sester 2010, Rabiei et al. 2016). The need for reliable rainfall data might not be fulfilled using only conventional data sources, whereas merging all possible data sources such as raingauges, radar data, crowdsourced observations, etc. , may provide a better rainfall estimation. This would result in a better understanding of the hydrological cycle, which helps to increase our knowledge and, consequently, to improve the quality of hydrological analyses. A methodology to estimate distributed rainfall was developed from satellite-based soil moisture observations by  Brocca et al. (2014). The method is named SM2RAIN and relies on the inversion of the soil water balance equation considering the soil as a natural raingauge.
(2016b)  demonstrated the potential of CMLs for long-term, large-scale operational rainfall monitoring, based on a 2. 5-year dataset from a cellular communication network consisting of about 2000 links covering the land surface of the Netherlands (35 000 km 2 ). Good results were obtained particularly for the summer season, which shows the potential of this opportunistic technique for large-scale application in developing countries in (sub)tropical climates, with few rain gauges and little or no weather radars (Fig. 2 ). Innovative approaches to estimate point-wise rainfall include moving cars and imaging. Rabiei et al. (2013) used moving cars for short time step rainfall estimation. The ubiquitous diffusion of cars motivated the idea of exploiting them as moving rain gauges with windshield wipers or optical sensors as rainfall measurement devices.
Since the establishment of MOXXI, progress has been achieved in the observation of surface waters in difficult-to-access environments and during extreme events through diverse multi-disciplinary techniques. For instance, to partially address the issue of observing stream processes in the case of scarce measurement stations, the use of satellite nightlight data has been explored. Nocturnal lights in the proximity of rivers proved to be consistently linked to flood damage; further, increasing nightlights has been associated with flood damage intensification  (Ceola et al. 2014). Also, participatory data collection methods and data mining have proved promising to expand the dataset currently available to the research community. For instance, water level time series have been derived from the analysis of several YouTube videos for a Saudi Arabian cave  (Michelsen et al.
2014 ). The SMAR model has been coupled with the ensemble Kalman filter (EnKF) to reduce the regional-scale bias of satellite near-surface moisture data  (Baldwin et al. 2017). The SMAR-EnKF system can predict root zone soil moisture over broad extents, and has applications in drought prediction and for other operational hydrological modelling purposes. Ongoing data-model integration methods are combining satellite-sensed and field-based measurements with models to better predict soil moisture and interactions in ecohydrology  (Hwang et al. 2008 , Ng et al. 2009). Within passive remote sensing, some methods employ thermal and visible/near-infrared bands to determine soil surface water content. The thermal inertia method  (Xue and Cracknell 1995 , Sobrino and El Kharraz 1999 , Maltese et al.
2007). A surface renewal method to estimate sensible heat flux requiring only LST and a few meteorological forcings as input (Castellví et al. 2016) was derived under unstable conditions over a heterogeneous canopy. By using multi-temporal thermal acquisitions from satellites, aerial surveys, or drones, the method overcomes the inconsistency between LST and mean aerodynamic temperature. The Atmosphere-Land Exchange Inverse (ALEXI) and the related disaggregation algorithm (DisALEXI) package provides self-consistent evapotranspiration retrievals as well  (Anderson et al. 2011). ALEXI is a multi-sensor TIR approach to evapotranspiration mapping, coupling a two-source land-surface model with an atmospheric boundary layer model in time-differencing mode. DisALEXI spatially downscales ALEXI fluxes to finer spatial scales through higher spatial resolution TIR imagery.
Hicks et al. (2015)  likewise developed the Arduino-based Mayfly data logger as a telemetry enabled platform for use in hydrological monitoring networks, and it is an integral part of the EnviroDIY community for development of environmental monitoring technologies. Wireless sensor network (WSN) technology enables the distributed sensing of hydrological states and fluxes at the catchment scale  (Inagaki et al. 2011 , Jin et al. 2014). WSNs consist of a multitude of small sensor nodes embedded in the environment that can quantify spatially heterogeneous phenomena, such as temperature or soil moisture fields and vegetation conditions at high spatiotemporal resolution  (Qu et al. 2014 , Wiekenkamp et al. 2016). One of the first low-cost mesh networking proprietary standards used for WSNs is ZigBee  (Valente et al.
Collaborative efforts Citizen science and participatory platforms constitute promising approaches for massive collection and analysis of hydrological data. For instance, an increasing number of personal weather stations (PWSs) automatically upload rainfall measurements to online platforms, particularly in urban areas. De Vos et al. (2017) examined the potential of such crowdsourced data for obtaining high-resolution rainfall measurements for the Amsterdam metropolitan area, the Netherlands. A detailed analysis of rainfall data from more than 60 stations over an area of nearly 600 km 2 during several months confirmed that the growing number of internet-connected PWSs can be used successfully for urban rainfall monitoring. Remaining challenges include accounting for uncertainties in the PWS data resulting from both the imperfect measurement set-up and rounding and threshold processes that occur during the data transfer to the online PWS platform.
The rain is recorded like a classical self-emptying rain gauge, while the system retains about 6 cm of water in the wick, which is monitored between rainfall/irrigation events as a measure of evaporation. By comparing the observed evaporation to the measured wind, humidity, solar radiation and temperature, discrepancies in predicted and observed values can be used as indicators of faulty sensors. Further, the observed evaporation is highly informative with respect to hydrological processes. Within TAHMO weather stations, a GPS, compass and accelerometer are also included to allow control of the continued proper installation of the station. The sensor suite and communication module are powered by a single embedded solar cell the size of a business card. Data are communicated between 4 and 24 times per day through GPRS (mobile phone).
The stations have some redundancy to further allow quality control. Automated QA/QC is performed on the TAHMO servers. In addition to the novel technology, TAHMO distinguishes itself from earlier initiatives by actively building public-private partnerships (PPPs) with national meteorological agencies. Memoranda of understanding (MoUs) are now in place in 13 African countries. Although noncommercial data use for science and government is free, commercial usage is subject to a fee to make the system financially sustainable. The PPPs serve to both strengthen the national meteorological agencies and develop new business models and activities. Finally, it is important to note that TAHMO stations are mainly being installed at secondary schools, where they are linked to the science, technology, engineering and mathematics curriculum.