dataesr/scientific-paragraphs-categorization huggingface dataset:
Each replicate was composed of 12 first instar stink bugs in plastic boxes (11 x 11 x 3. 5 cm), totaling 48 stink bugs per treatment (4 replicates of 12 insects each). First instar nymphs were maintained inside plastic boxes (11 x 11 x 3. 5 cm) lined with moistened filter paper, and provided with the respective food sources, together with plastic Eppendorf® microtubes equipped with water-soaked cotton balls. Food was replaced and evaluations were made daily. When the nymphs reached adulthood, weight (mg) and pronotum width (mm) of the insects were evaluated after 24 hours of adult emergence. The insects were weighed using a precision analytical scale (Shimadzu, model Ay220), and the pronotum width was measured using the Image J -Version 1. 47 software on photographs taken with a stereoscopic microscopic of 40x magnification, using the software Leica Application Suite.
Landscape metrics A Pleiades satellite image was acquired on January 16th, 2013, with a ground resolution of 0. 5 £ 0. 5 m in the panchromatic mode, and 2 £ 2 m in the multispectral mode, with blue (B), green (G), red (R) and near-infrared (NIR) bands. In March 2013, a land-cover field survey was conducted in the study area to identify main vegetation types. The 'multi-resolution segmentation' algorithm in the ENVI Feature Extraction Module 5. 1 was used to obtain meaningful landscape objects from the image. With a sample of 835 ground truth sites (out of the 1462 visited ones) as training data, the large image-object scale was classified into five land cover classes including rangelands, trees, crops, water bodies, built up areas, and roads. The classification accuracy was evaluated using ground truth data (732) that were not used in the classification process.
The land-cover map validation showed a good match between the predicted and observed classes with a global accuracy rate of 80. 7% of ground truth pixels correctly classified. Then, from the land cover map, three landscape variables were calculated at four different spatial scales (from 250 to 1000 m-radius) around the set of 24 focal millet fields, using the Fragstat software  (McGarigal, Cushman, Neel, & Ene, 1995) : (i) number of tree patches, (ii) area covered by rangelands, and (iii) Shannon diversity index (H') of vegetation classes including crops, trees, and rangelands (Fig. 1B ).
This approach consists in fitting all possible linear combinations of the predictors and ranking the models according to fit balanced by a penalty for increasing numbers of parameters  (Rusch et al. , 2011). For a data set with k explanatory variables, there were 2 k combinations among variables (here, 8 to 16 models resulting from 2 3 to 2 4 combinations). We used this approach for each data set at the four spatial scales. Candidate models were ranked using the second-order Akaike information criterion (AICc) which is a biased-corrected version of the AIC recommended in analyses where the sample size is small relative to the number of fitted parameters  (Burnham & Anderson, 2004). To find out which spatial scale was most important, we considered all 2 k combinations for each spatial scale in the same dataset and recalculated the normalized Akaike weights on this new dataset for each model (i.
e. , considering the AICc min among the dataset including all combinations of predictors at each spatial scale) (see Appendix A: Fig. A2 ). The relative importance of an explanatory variable at a given spatial scale was computed as the sum of the Akaike weights across all the models in the set where this variable occurred  (Burnham & Anderson, 2002). The final model was obtained by combining predictors at the most relevant spatial scale in a generalized linear model (GLM). In each final model, year was considered as a fixed effect when the effect of year was significant. Models were fitted using the appropriate distribution type and link function: binomial for proportions (logit) and Poisson for count data (log). The "MuMIn", "nlme", and "Mass" R packages were used. To examine complex cause-effect relationships between landscape features, crop colonization or natural regulation, and pest incidence, we used partial least squares structural equation patch modelling (PLS-PM).
Acknowledgments Hymenopteran parasitoids were sorted and identified based on morphological traits with the support of  G erard Delvare  from the  Centre de Biologie pour la Gestion des Populations (CBGP, Montpellier, France). The land cover map was performed by  Mame Diarra Diop  from the  Centre de Suivi Ecologique (CSE)  within the framework of the  RECOR  project ( WAAPP-PPAAO program ) coordinated by  ISRA (Institut S en egalais de Recherche Agricole). The satellite images were obtained through the  ISIS French program  ( CNES, Toulouse, France ). The open access publication fees were supported by the platform in partnership Diversit e Biologique et syst emes agro ecologiques, Sant e des sols et des cultures (DIVECOSYS, CIRAD) and the mixed research unit ASTRE (CIRAD).
Funding This study was partially supported by the  Service de Coop eration et d'Action Culturelle of the French Embassy in Senegal, CIRAD (Action incitative) , and  IFS  N o D/5906-1 ( International Foundation for Science, Sweden). It was also funded by the  CGIAR   Research Program  on  Grain Legumes  and  Dryland Cereals  (Program FP3-3. 3,Managing functional biodiversity and crop pests) and supported by  CGIAR Fund Donors.
These devices use LoRaWAN communication technology  [4]  in many sectors  [6]. Figure  1  illustrates the different applications of the Smart Paesi integrating energy production tools (biomass heating, micropower plants) connected buildings, weather stations, agriculture and livestock, water and air quality. The Smart Paesi information system, illustrated in Figure  2 , is intended to perform several actions: • the information will be used by a computational model called Smart ENTITY  [7] , using supervised machine learning libraries and optimization algorithms to help predict and optimize the observed systems; • the collection of two types of information: 1) Information from connected objects: energy production and metering systems; LoraWAN wireless sensor network, 2) Information from field surveys of the population on their needs expectations; • storage of information from connected objects in a NoSQL database based on indexing, using the Elasticsearch engine for dynamic data search; • the information will be used by a computational model called Smart ENTITY  [7] , using supervised machine learning libraries and optimization algorithms to help predict and optimize the observed systems; • the visualization of the data from a browser will be done according to dashboards built with the help of the complementary data visualization tool of Elasticsearch, Kibana.
Smart Agriculture applications The Smart Farm and agriculture applications of the Smart Village are built around two major use cases : -monitoring of livestock or dairy production using devices transmitting GPS data over the LoRa/LoRaWAN network using LoRa GPS Device  [5]  -support for organic saffron cultivation using environmental sensors and soil data measurement (temperature and humidity). From these data, dashboards are developed to help manage farms. As part of animal monitoring, as shown in Figure  4  the first devices have been placed on pigs' necks, using LoRa/LoRaWAN technology. This application allows the farmer to have the position of these animals (raised in freedom on plots of several hundred hectares). The positions, recorded progressively, are stored in a database and allow the animals to be tracked in real-time to identify the slightest problem (immobile for too long).
Evolution of the Information System The current Smart Village information system represents an experiment in the actual deployment of a single information system for the village and its agricultural activities. However, the environmental and geographical conditions affect the quality of service of the information system. Indeed, LoRa/LoRaWAN technology is influenced by temperature  [9]  and vegetation. Indeed for this last point, it appears from many experimentations  [10, 11, 12, 13]  that vegetation could reduce the signal propagation and the range at which a message can be transmitted: from 500 meters in an open field, the signal reached about 90 meters in a forest, this kind of impact needs to be taken into account as it could severely damage the reception of the data. We also observe corrupted data storage (missing data, empty message) and sometimes erroneous data.
This problem has the effect of overloading the Bastia datacenter in terms of data storage and causing unusable data to be transmitted over the network. In order to resolve these disturbances, to limit the flow of corrupted data back to the data centre and an overload of bandwidth, we envisage a new structuring of the information system. We are thinking about a layered structuring of the in layers according to the generic model presented by  [8]. In this generic model, we find four layers: • an IoT/Sensors layer, with the collection or actuator entities deployed on the territory; • an edge layer with gateways such as dedicated base stations located near IoT devices, which act as a gateway to the fog or cloud layer. This organisation reduces traffic, and wa can find a microdata center, called cloudlets, can collect the sensed data from the IoT, filter it and only send a specific type of data.
Conclusion This article presented the structuring of the Smart Village information system, based in particular on a LoRa/LoraWAN sensor network, a scientific programme supported by the University of Corsica. Limitations in the current system structuring, particularly in the quality of the data sent to the data centre, have been identified. These limits are often linked to climatic conditions that impact the quality of communication. In order to reduce these problems, we propose redefining the structuring of our IS with an Edge layer and a Fog layer and integrating our calculation elements. We identify in advance the studies to be carried out and the elements to be secured.
Study design, patients This retrospective observational study was performed in five hospitals, four in the Paris area and one in eastern France. Based on ICU discharge diagnostic code and on laboratory database, all consecutive patients with laboratory-confirmed SARS-CoV-2 infection admitted to one of the ICUs between 21 February and 31 December 2020, were enrolled. Laboratory confirmation of SARS-Cov-2 was defined as a positive result of real-time reverse transcriptase-polymerase chain reaction (RT-PCR) assay of nasal and pharyngeal swabs. The study was approved by the ethics committee of the French Intensive Care Society (n. CE SRLF 20-89), which waived the need for informed consent from individual patients due to the retrospective nature of the study. The study complied with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement guidelines (http://www.
Data collection Data were recorded by in-charge intensivists at each hospital from medical records and electronic reports. Baseline information collected at ICU admission were: date of ICU admission, age, gender, body mass index (BMI), date of the first symptom, dates of hospital and ICU admission and comorbidities. Regarding comorbidities, chronic obstructive pulmonary disease was defined as previously reported  [20] , overweight was defined by a BMI >25 kg. m -2 and obesity was defined by a BMI >30 kg. m -2. Immunosuppression included patients with solid tumors, hematological malignancies, solid organ transplantation, long-term immunosuppressive drugs, or HIV infection  [21]. Chronic kidney disease (CKD) was defined as stage 2 to 5 according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines  [22].
The author received no specific funding for this work. support from  Fisher & Paykel , grants from  French Ministry of Health , Fartoukh reports non-financial support from  Biomerieux , personal fees from  Pfizer  outside the submitted work, and grants from  French Ministry of Health. G. Louis reports other from  Pfizer , other from  Fresenius , outside the submitted work. E. Azoulay  has received fees for lectures from  Gilead ,  Pfizer ,  Baxter , and  Alexion , and his research group has been supported by  Ablynx ,  Fisher & Paykel ,  Jazz Pharma , and  MSD. S. BioMe  ´rieux, grants from  SOS Oxygène , grants from  Janssen , outside the submitted work. Y. Picard  has no conflict of interest to report. A. Vieillard-Baron  reports research grant from  GSK  company. M. Darmon declares having received a grant from  MSD , speaker fees from  MSD ,  Astellas , and  Gilead-Kite  and having attended an advisory board for  Gilead-Kite.
INTRODUCTION The cellulose content of land plants constitutes around 150 to 200 gigatons of carbon, roughly 1. 5-to 2-fold more than the rest of life forms on Earth combined  (1). This remarkable amount of cellulose, the main load-bearing component of plant cell walls, is synthesized by cellulose synthase complexes (CSCs). The CSC is a sophisticated molecular machinery that propels itself forward through the plasma membrane with its own catalytic activity while using cortical microtubules as steering devices  (2). To date, four components of the cellulose synthase machinery have been identified: (i) the catalytic Cellulose Synthases (CESAs) (3); (ii) KORRIGAN, an integral membrane endoglucanase whose exact role is unknown (4); (iii) CSI1, which establishes a physical association between membrane-localized CSCs and cortical microtubules  (5) ; and (iv) the CESA-COMPANION (CC) proteins, essential for the stability of CESAs and cortical microtubules upon salt stress and fungal interaction  (6).
Spinning disk live-cell imaging and data processing Three-day-old plant hypocotyls or 5-day-old roots were covered with a 1% agarose cushion and mounted onto the imaging system as described previously  (31). Fluorescence-tagged proteins were imaged with a CSU-W1 T1 Yokogawa spinning disk head fitted to a Nikon Eclipse Ti2-E inverted microscope with a CFI Apo TIRF × 100 numerical aperture (NA), 1. 49 oil immersion objective, and two iXon Life 888 EM-CCD cameras (Andor, GB). The system was assembled by 3i (www. intelligent-imaging. com). GFP was imaged using a 488-nm solid-state diode laser (150 mW) and a 525/30-to 25-nm emission filter, and red fluorescent protein (RFP) was detected with a 561-nm solid-state diode laser (150 mW) and a 617/73-to 25-nm emission filter. Alternatively, a CSU-W1 Yokogawa spinning disk head was fitted to a Nikon Eclipse Ti-E inverted microscope with a CFI PlanApo × 100 NA, 1.
Statistical analysis and experimental design For statistical analyses, Welch's unpaired t test, two-tailed unpaired t test, one-way analysis of variance (ANOVA), Welch's ANOVA, or two-way ANOVA was performed using GraphPad Prism 9. 3. If appropriate, Dunnett's T3 multiple comparisons test or Tukey's multiple comparisons test was performed afterward. A P value of <0. 05 was considered as statistically significant. Statistical methods used to calculate P values are described in the figure legends. Data analysis (especially for images) was either done automatically, or file names were removed before the analysis. For the measurement of plant size, investigators were not blinded. In this case, data were always collected according to the genotype of plants. Sample size was determined for each experiment based on similar data reported in scientific literature.
Acknowledgments: We are grateful to  K. Scklodowski  for generating the CESA constructs used for the Y2H assays and to  O. Voinnet  for critical reading of the manuscript. Live-cell imaging was performed with equipment maintained by the  Scientific Center for Optical and Electron Microscopy (ScopeM, ETH-Zürich ) and by the  Center for Advanced Bioimaging (CAB) Denmark. Funding: This work was funded by the  Spanish Ministry for Science and Innovation  ( MCIN/AEI/ 10. 13039/501100011033 ,  PGC2018-098789-B-I00 , and  PID2019-107657RB-C22  to M. A. B. , N. R. -L. , and A. G. C. , respectively), the  Andalusian Research Plan  cofinanced by the  European Union  ( PAIDI 2020-PY20_00084  and  UMA20-FEDERJA-023 ) to M. A. B. and  Junta de Andalucía UMA-FEDER  project ( UMA18-FEDERJA-154 ) to N. R.
Remarkably, HIFlow is able to reproduce the CAMELS average and standard deviation HI power spectrum (Pk) within a factor of 2, scoring a very high R 2 > 90%. HIFlow will enable the testing of Pk pipelines for HI surveys, and assist in computing other statistical properties beyond Pk that require generating new diverse samples of high dimensional datasets, such as the covariance matrix. This new tool represents a first step towards enabling rapid parameter inference at the field level, maximizing the scientific return of future HI surveys, and opening a new avenue to minimize the loss of information due to data compression.
We choose the HI fields since many of the upcoming future surveys aim to map out the HI distribution in the early Universe to trace the large-scale structure. To train our emulator, we use the HI maps that are generated in a similar way as described in the CAMELS Multifield Dataset 1  (CMD, Villaescusa-Navarro et al. 2021d) but at z=6 with a lower resolution. The state-ofthe-art CAMELS simulation contains thousands of HI maps generated using SIMBA  (Davé et al. 2019)  and IllustrisTNG  (Weinberger et al. 2017; Pillepich et al. 2018)  simulations. We here focus on the HI maps generated using the IllustrisTNG simulations. We first train unconditional HIFlow and test its performance by comparing with real maps from CAMELS in terms of several summary statistics such as the probability distribution and the power spectrum.
SIMULATIONS We use simulations from the CAMELS project, that have been recently introduced in Villaescusa-Navarro et al. (  2021c ). Here, we briefly describe CAMELS and refer the reader to  Villaescusa-Navarro et al. (2021c)  for further details on the different simulations and datasets. CAMELS is a suite of thousands of simulations run with state-of-the-art cosmological hydrodynamic galaxy formation models, namely Simba  (Davé et al. 2019)  and IllustrisTNG  (Weinberger et al. 2017; Pillepich et al. 2018) , by varying two cosmological parameters (Ω m and σ 8 ) and four other parameters that modify the strength of stellar feedback (A SN1 , A SN2 ) and black hole feedback (A AGN1 , A AGN2 ) relative to the original IllustrisTNG and Simba simulations. We focus our analysis on two CAMELS sets, namely, the 1 parameter (1P) set which varies a single parameter at a time with the same initial seed number, and the Latin-hyper-cube (LH) set, which is a set of 1,000 simulations that explores this six-dimensional parameter space with prior ranges defined as: Ω m ∈ (0.
4 h -1 cMpc at z∼6. This means we can generate five distinct HI maps along each of the three di-rections x, y and z per simulation. In total, our dataset contains 15,000 HI maps from the 1,000 simulations of the CAMELS LH set. We use 900 simulations (or 13,500 maps) for training, and 50 simulations (or 750 maps) for validation and testing each. We flatten all maps and transform the data to have a range from -1 to 1. Our best performing MAF consists of 10 autoregressive layers (10 MADE). Each MADE consists of 3 hidden layers of sizes 1024, 2048, and 4096, and each conditional is parameterized as a mixture of 10 Gaussians. Following the terminology by  Papamakarios et al. (2017) , our design is called MAF MoG (10). We use the hyperbolic tangent as an activation function throughout, Adam  (Kingma & Ba 2014 ) as an optimizer, with a minibatch size of 100, a learning rate of 10 -4 , a small weight decay rate of 10 -6 , and early stopping is applied if no improvement is observed for the 30 consecutive epochs on the validation set.
In this case, advanced architectures, such as the Neural Spline Flows  (Durkan et al. 2019) , Generative Flow with Invertible 1×1 Convolutions (Glow,  Kingma & Dhariwal 2018)  or the Vector Quantized Variational Autoencoder (VQ-VAE,  Razavi et al. 2019) , might be needed. We leave investigating more complex architectures with more complex data sets to future work. HIFlow enables many applications including testing power spectral pipelines of HI surveys and assisting in computing statistical properties that require many field samples, such as the covariance matrix. HIFlow is an initial step towards studying the non-Gaussian nature of HI maps, performing rapid parameter inference at the field level, efficient HI forecasting for future large scale and intensity mapping surveys, and thereby maximizing the scientific return of observations by the next generation of facilities, such as Roman and SKA.
ACKNOWLEDGEMENTS The authors acknowledge very helpful and extensive discussions with  George Papamakarios (DeepMind) , which have improved the paper significantly. The authors also acknowledge comments provided by  Dylan Nelson ,  Kaze Wong , and  Adrian Price-Whelan. SH, FVN, BW, DNS, DAA, SG, BLB, and RSS acknowledge support provided by  Simons Foundation. FVN was supported by funding from the  WFIRST program  through  NNG26PJ30C  and  NNN12AA01C. DAA was supported in part by  NSF  grants  AST-2009687  and  AST-2108944. GLB was supported in part by  NSF  grants  OAC-1835509 ,  AST-2108470. SA acknowledges financial support from the  South African Radio Astronomy Observatory (SARAO).
Optimal a and b parameters are identified by means of the "Nonlinear Least Squares" method of MATLAB, see Tables  6  and 7  for the results. The lowest coefficient of determination, R 2 , amounts to 0. 9992 and is obtained for the deviatoric components of strain as well as stress concentration tensors of LDCR hydrates, see Tables  6  and 7  as well as Figs. 8  and 9. Notably, the a parameters found for the strain concentration tensor components are identical to the b parameters of the corresponding stress concentration tensor components, and vice versa, because strain and stress concentration tensor components are linearly related via Eqs. (  49 ) and (50).
Aim: Primary sleep bruxism (SB) affects between 6 and 30% of children and adolescents. Its frequency increases with age, reaching a peak at 10-14 years of age, after which it drops until adulthood. The treatment of SB has not been extensively documented, resulting in a lack of reference points and legitimate concerns for dentists in their everyday practice. The aim of this literature review was to summarize the available evidence on the management of SB in children and adolescents and the efficacy of the different approaches. Method: A systematic literature search was conducted according to PRISMA guidelines from January 2006 to December 2020 using the PubMed, The Cochrane Library, Science Direct, and Google Scholar databases. All types of SB treatment were accepted. Eight studies were selected for their protocol quality, according to the PICOS tool.
Inclusion and exclusion criteria Inclusion criteria comprised comparative or noncomparative, prospective or retrospective, longitudinal data related to the clinical treatment of bruxism in children. The inclusion criteria for the paper review were: -Articles published in English from January 2006 to December 2020. -Children and adolescent participants, with an age range of 1 -18 years from both genders and with primary, mixed, and secondary dentitions. The diagnostic criteria of bruxism were: -Patients' parents having reported teeth-grinding or teeth-clenching. -(and/or) Bruxism, confirmation by a dentist or practitioner using diagnostic criteria, such as tooth wear. -(and/or) Possibly, the use of polysomnography to confirm the clinical diagnosis. -All types of interventions aiming to reduce bruxism in children were accepted, whether they were pharmacological, psychological, physical, surgical, or therapeutic via occlusal splint.