dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods We used pooled data from several SMART surveys carried out by the Government of Ethiopia and UNICEF, covering 4 years (2016-2019). Data on weight, height, MUAC, gender and age were available for a total of 26,806 Ethiopian children, from SMART surveys conducted in 49 different woredas/districts from 6 provinces of Ethiopia between 2016 and 2019. A two-stage cluster sampling approach based on a population sampling frame probability proportion to size (PPS) of all smallest rural villages was used as per SMART survey methodology. The sample size for anthropometric and retrospective mortality survey was determined by entering expected prevalence of malnutrition and mortality rate, desired precision, design effect, percentage of children under five, average household size and per cent of non-responsive households.
Data quality was maintained by provision of standardization test and field practice during training, field level supervision by survey coordinators during data collection, daily checking and exchanging feedback on completed questionnaires and measurement error. The plausibility check in the ENA ensured that the quality of these data was within the expected quality standard. All parents of participants gave their written consent. The target population was children aged 6-59 months. Children's height, measured to the nearest 1 mm (UNICEF measurement boards) were collected from each child. Weight was measured to the nearest 0. 1 kg (Seca, Hamburg, Germany), with children wearing only light clothes. The nutritional status of children was defined using MUAC, length or height-for-age (L/HAZ), WHZ, and weight-for-age (WAZ) z-scores, calculated according to the Child Growth Standard of the WHO  [10]  using the WHO Anthro software.
The sensitivity and proportion of falsepositive (1-specificity) of MUAC were determined using wasting (WHZ<-2 z-score in children under 5 y) as acute malnutrition is the combined case load of low MUAC and/or low WHZ. The ROC curve is the plot of sensitivity versus proportion of false-positive of MUAC cut-offs. The area under curve (AUC) is the area between the curve and the segment (0,0) and (1,1), which corresponds to a random classifier. A larger AUC indicates a more accurate diagnosis of acute malnutrition defined by WHZ cut-offs  [12]. Data analysis was performed using SPSS version 20. 0 (SPSS, Inc. , Chicago, IL). To evaluate the performance of our analysis, the corresponding Youden index, which is the sum of sensitivity and specificity minus one, and reflects the overall capacity of an early warning model to detect a disease, was calculated: 1 indicating a perfect test, and 0 an imperfect test  [13].
The partial differential equations were solved using pdepe function in Matlab. For this purpose, the D e value was changed iteratively to minimize the root mean square error (RMSE) between experimental and predicted contents of total phenolics in almond skin  [24] : RMSE mg g = 1 n n i=1 C S,p (t) -C S,e (t) 2 (11) where C S,e and C S,p is the experimental and calculated values of total phenolic content in almond skin (mg/g), respectively. n refers to the number of experimental data. To further validate the accuracy of diffusional model, R 2 (coefficient of determination), RMSE and mean relative deviation modulus (%) were calculated based on the data about extraction yield after the optimization of D e  [25] : R 2 = 1 - n i=1 Y e -Y p 2 n i=1 (Y e -Y m ) 2 (12) RMSE mg g = 1 n n i=1 Y p (t) -Y e (t) 2 (13) E (%) =         n i=1 Y e -Y p /Y e n         × 100 ( 14 ) where Y e and Y p are the experimental and predicted extraction yields of total phenolics (mg/g).
In order to explore the influence of the operating parameters required during the extraction, a CCD was used to analyze the total polyphenol content (TPC) and the skin separation time (SST). Fully coded experiments and responses obtained for each run of the CCD are represented in Table  1. Responses vary according to the system variables (from 0. 024 to 0. 063 of mg naringenin equivalent for a gram of almond, and from five to twenty minutes of skin separation time). Similar results for experiments 2, 4, 7 to 9 and 13 (central points) are indicators of data quality and statistical reliability of key variables. Significance and suitability of the design were studied using an analysis of variance (ANOVA). The statistical significance of each effect (including interaction terms, linear, and quadratic effects) was determined using the probability p (p-value) given in Table  S1.
6. 3. PCR Amplification: Specific nucleotide primer sequences (see Table  1 ) were designed using OLIGO 7 software (Medprobe, Oslo, Norway) and synthesized by Eurogentec (Angers, France). Amplification was detected by SYBR ® Green fluorescence on an ABI PRISM ® 7900HT sequence detection system (Applied Biosystems, Courtaboeuf, France). Thermocycling was carried out in a final volume of 20 µL. The thermal cycling conditions were 2 min at 50 • C, then 10 min at 95 • C, followed by 40 amplification cycles at 95 • C for 15 s, at 60 or 65 • C for 45 s and at 95 • C for 15 s. qPCR melting curve analysis was performed to confirm the specificity and efficiency of selected primers.
Statistical Analysis Data were analyzed with GraphPad Prism ® 6. 0 software (San Diego, CA, USA). The results are expressed as mean ± SD. A one-way ANOVA with Tukey's multiple comparisons test was used when performing the relative quantification and comparison of gene expression by qRT-PCR. Unpaired t-test was used when assessing the effect of an inhibitor or age difference (Figures  4  and 5 ). Statistical significance was set at p < 0. 05 for all the tests.
e. , matrix effect). The glycemic response will be more important after intake of a meal rich in refined carbohydrates, and poor in fibers, fat, and proteins  [19, 20]. Thus, meals that are generally higher in refined carbohydrates, such as breakfast and snacks, may induce a higher glycemic response and consequently increase the risk of cognitive decline. However, the effect of the GL of the different meals on the different cognitive domains has been rarely studied. In the present study, we determined whether high-GL diet was associated with cognitive changes in the different cognitive domains during a 12-year follow-up using data from a large population-based prospective cohort study (the Three-City study). We evaluated the role of APOE4 status on the association between GL and cognitive changes, and the impact of the GL of each meal type (daily, breakfast, lunch, afternoon snack, and dinner) on this association.
Study Sample Participants (n = 9294) were recruited from the Three-City (3C) cohort of community-dwelling individuals, aged 65 years and over, from the electoral rolls of three French cities (Bordeaux, n = 2104; Dijon, n = 4931; and Montpellier, n = 2259), enrolled between 1999 and 2001. A detailed description of the 3C study has been provided elsewhere  [21]. The Ethical Committee of the University Hospital of Kremlin-Bicêtre (France) approved the 3C protocol (Project no. 99-28, June 1999), and all participants signed an informed consent. Participants from the Bordeaux and Montpellier 3C centers had face-to-face interviews with trained nurses and neuropsychologists at baseline, and at 2, 4, 7, 10, 12 and 15 years of follow-up. Standardized questionnaires were used to collect data on socio-demographic, clinical and lifestyle characteristics.
Dietary Data Dietary data were collected using the 148-item FFQ. The FFQ was divided into breakfast, lunch, dinner, and snacks between meals, and was described elsewhere  [16]. For each FFQ item (i. e. , food/beverage categories), the GL was calculated as the product of the glycemic index (GI) and the amount of available carbohydrates in a standard serving size, divided by 100  [22]. Foods with low carbohydrate content (e. g. , meat, fish, fats, vegetables) were assigned a null GL value. GI values and serving sizes were obtained from the International Table of Glycemic Index  [23]  and internet updates  [24] , using glucose as a reference. Then, the GL of each item was multiplied by the reported frequency of that item per week. Finally, the sum of this last value for all the items consumed during a meal gave the total GL for breakfast, lunch, afternoon snack ("goûter" in French, corresponding to a snack between lunch and dinner), and dinner.
Main Analyses Linear random-effect models were used to evaluate the association between GL and cognitive score changes over the 12-year follow-up. We used the lme function from the nlme R package with random intercept and slope (time), and unstructured covariance structure. Normal distributions of cognitive scores were checked with histograms. To normalize distributions, TMT scores were transformed using the natural logarithm function. The linearity assumption between cognitive score and GL was assessed by testing the loglikehood difference between two models: one model with GL tertiles as continuous variable and one model with GL tertiles as categorical variable. As the relationship between GL and cognitive scores was nonlinear, cut-offs were chosen a priori according to tertiles. The GL categories were defined as low, middle and high tertile.
Fertility assessment In the evening before spawning, groups of 1 female:2 males were transferred to 3 L spawning tanks with insert (Tecniplast®) and left overnight. The day after (approx. 9 a. m. ), spawns from treated and control groups were collected and fish were transferred back to the maintenance system. No significant effect on spawning success, fertilization rate, and 24 h embryo survival was observed (data not shown); therefore, fertility was further evaluated as the average number of eggs laid per female only. For each generation, fertility was assessed for approx. 10 weeks between 3 and 5. 5 month of age, 4-7 replicates/ treatment/week. Compared to F1 and F2 generations, the analysis of F0 fertility included much less early spawns, which lead to a higher baseline count of eggs laid per female.
Statistical analyses Statistical analyses of data from each generation independently were performed using analysis of variance (ANOVA) in R (v3. 6. 1). Residuals from models derived from all datasets were normally distributed, only slight deviation from normality was tolerated due to the large sample size. Multiple ANOVA (MANOVA) with 3 factors (generation, treatment and sex) was applied on morphometric variables (body length, weight). ANOVA 2 factors (treatment, generation) was performed on reproduction data. Due to independency between LPMR experiments, ANOVA 3 factors (generation, control/treated, and light status) was applied separately on each of the 4 treatments. For all, results showing a p-value ≤ 0. 05 between treated and control samples in post-hoc analysis (Tukey's method with p-value adjustment for multiple comparisons) were reported as significant.
Lipidomic data were analyzed using basic partial correlation analysis on the whole dataset following normalization (mean-centering and log-transformation). Then, ANOVA 3 factors (treatment, generation, sex) was applied on organ-specific data. P-values ≤ 0. 05 (Tukey's adjusted) were considered as significant correlations/ differences. Figures were generated using either Graphpad Prism 5, DisplayR, or Metscape (v3. 1. 3) and Cytoscape (v3. 7. 2)  (Karnovsky et al. , 2012; Shannon et al. , 2003).
Lipidomic analyses We analyzed lipid profiles in brains, livers, and gonads of PL and PH adults. Partial correlation analyses revealed that permethrin exposure was overall correlated with a reduction in lysoPC content (Fig. 4A , corr. coeff ρ=-0. 126). However, additional statistical analysis did not allow drawing clear conclusions on the effect of the treatment on lysoPC concentrations (Fig. 4B , Section S7). It is also of note that permethrin exposure was significantly correlated with fish weight and size (Fig. 4A ). Details on data are provided in Appendix B. Other lipid classes did not show any significant change upon exposure.
(2017)  and crystalline AAs were assumed to be 100% digestible. Data collection. Feeder and BWs were captured on the 56th days in order to calculate the feed intake (FI), BW gain (BWG) and feed conversion ratio (FCR) over the experimental period. Mortality was recorded and FCR was adjusted according to the feed consumption, dead broilers, and considering the days of mortalities conform described by  Sakomura and Rostagno (2016). Statistical analysis of results. All statistical procedures were performed with the statistical software SAS 9. 0 (SAS Institute Inc. , Cary, NC, USA), in which data were analyzed as two-way ANOVA. The main effects were Met sources (DL-Met and L-Met) and proportions of Met and Cys (Met : Cys ratios) as well as the interaction between both factors. Only the main effects were considered when there were no interactions.
At the end of the trial, feed refusals were measured to correct for tracers that were not consumed. Data collection. Three broilers were euthanized with carbon dioxide as reference to determine the natural enrichment at the beginning of the experiment (42 days of age). At 56th day of age, all broilers fed L-( 15 N)-Met or L-( 15 N 2 )-Cystine for 14 days (42 to 56 days) were euthanized in order to collect the enriched tissues. The amount of digesta present in the gastrointestinal tract was manually removed and the whole body samples were ground and homogenized to obtain a representative sample of the whole body. All the excreta were collected daily, pooled, homogenized and sampled for analysis. All the samples were weighed and conserved at -20°C for further analysis. Processing sample and chemical analysis.
Acknowledgements The authors would like to thank  FAPESP  for the financial support  -Brazil  (grant number  2013/25761-4 );  Conselho Nacional de Desenvolvimento Científico e Tecnol ógico for the scholarship -Brazil  ( CNPq , grant number  141614/ 2017-0 ) and to  Coodernação de Aperfeiçoamento de Pessoal de Nível Superior  for the  Doctoral Sandwich grant -Brazil  ( CAPES, Finance Code 001 ). This study was derived from the PhD thesis of the first author. R. M. Suzuki  0000-0001-8106-023X L. G. Pacheco 0000-0002-7803-6746 J. C. P. Dorigam 0000-0002-6793-3636 J. C. Denadai 0000-0003-0853-1159 G. S. Viana 0000-0002-9489-5612 C. C. N. Nascimento 0000-0002-0838-7786 J. Van Milgen 0000-0002-6131-5255 N. K. Sakomura 0000-0001-5707-4113.
In many regions and at the planetary scale, human pressures on the environment exceed levels that natural systems can sustain. These pressures are caused by networks of human activities, which often extend across countries and continents due to global trade. This has led to an increasing requirement for methods that enable absolute environmental sustainability assessment (AESA) of anthropogenic systems and which have a basis in life cycle assessment (LCA). Such methods enable the comparison of environmental impacts of products, companies, nations, etc, with an assigned share of environmental carrying capacity for various impact categories. This study is the first systematic review of LCA-based AESA methods and their applications. After developing a framework for LCA-based AESA methods, we identified 45 relevant studies through an initial survey, database searches and citation analysis.
Performing the LCA-based AESA The practitioner should, as a general rule, adhere to best practice in classical LCA  (ISO 2006a (ISO , 2006b ). However, it is important to diverge from the predominant inventory analysis practice of expressing elementary flows in mass (or, occasionally, volume) and instead express them as an actual flowrate, i. e. a mass (or volume) per unit of time. This is necessary, since the carrying capacity in most cases relates to a rate of emissions or resource use (see section 2. 2). There are different ways of incorporating time information in life cycle inventories. For example, Ryberg et al (2018a) studied the continuous activity of laundry washing in Europe and expressed all elementary flows in kg (or m 3 ) per year. Recent developments in dynamic life cycle inventory databases may also be utilized  (Pigné et al 2019).
To further qualify discussions around the use of various sharing principles, social scientists studying approaches to sharing scarce resources and the degree of societal support should be invited to join the platform. Integration of LCA-based AESA in software is important to make its application easier. Compared to existing LCA software, LCA-based AESA requires a number of additional features: (1) management of time information for processes or elementary flows (see section 4. 2), (2) spatially differentiated calculations of environmental impacts for regional categories (also a requirement for state of the art classical LCA, Mutel et al 2019), (3) spatially differentiated calculations of assigned carrying capacity for regional impact categories, and (4) links to potential databases on carrying capacities and assigned shares of carrying capacity, according to different sharing principles (elaborated in section 5.
Also, a consensus on best practice for different methodological aspects may be sought, inspired by consensus processes for LCIA modeling, such as the one initiated by the Joint Research Centre of the European Commission  (Hauschild et al 2013). This may be particularly important if agreement is to be sought on the underlying ethical norms, equations, data inputs and appropriate application for the various sharing principles ideally involving collaboration with social scientists. This could lead to the construction of databases of assigned shares of carrying capacity for different principles (expressed, for example, per unit of production), spatially and temporally differentiated where needed. As alternative to seeking a consensus on best practice, Huijbregts' (2014) proposal of using a multi-model mean for default parameter values and allowing practitioners to incorporate variance between model outputs in uncertainty analysis could also be explored.
Acknowledgments We thank  Anastasia Wolff ,  French Ministry for the Ecological and Inclusive Transition , and  Reinout Heijungs ,  Leiden University , for their comments to an early draft of this paper. We also thank  Laure Patouillard ,  Polytechnique Montréal (CIRAIG) , for discussions leading to figure S1 and the two anonymous reviewers for their insightful comments.
ACKNOWLEDGEMENTS CQ and DC are supported by  INSERM. CQ is also supported by the  French society of Endocrinology (SFE, Socie´te´Franc¸aise d'Endocrinologie ,  Pfizer - SFE Prix de Recherche en Endocrinologie ) and by the  French Society of Nutrition  ( SFN Socie´te´Franc¸aise de Nutrition ). DC is also supported by  Nouvelle Aquitaine Region ,  Labex BRAIN   ANR-10-LABX-43 ,  ANR-10-EQX-008-1   OPTOPATH ,  ANR-17-CE14-0007   BABrain  and  ANR-18-CE14-0029  Mitobesity. XF is supported by  INRA ,  French society of Diabetes (SFD)  and  French society of Nutrition  ( SFN Socie´te´Franc¸aise de Nutrition ).
The pandemic of coronavirus disease 2019 (COVID-19) presents an unprecedented challenge to rapidly develop new diagnostic, preventive and therapeutic strategies. Currently, thousands of new COVID-19 patients are quickly enrolled in clinical studies. We aimed to investigate the characteristics of the COVID-19 studies registered in ClinicalTrials. gov and report the extent to which they have incorporated features that are desirable for generating high-quality evidence. On April 28, 2020, a total of 945 studies on COVID-19 have been registered in ClinicalTrials. gov; 586 studies are interventional (62. 0%), the most frequent allocation scheme is the parallel group assignment (437; 74. 6%), they are open-label and the most common primary purpose is the research on treatment. Too many of the ongoing interventional studies have a small expected sample size and may not generate credible evidence at completion.
Introduction The pandemic of coronavirus disease 2019 (COVID-19) caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents an unprecedented challenge to rapidly develop new diagnostic, preventive and therapeutic strategies  1. Currently, thousands of new COVID-19 patients present for care every day, and many are quickly enrolled in clinical studies. We aimed to investigate the characteristics of the COVID-19 studies registered in ClinicalTrials. gov 2 , and report the extent to which they have incorporated features that are desirable for generating high-quality evidence.
For this reason, I consider this brief report important for the scientific community. There are a few minor suggestions for the authors: Table  1 : The presentation of the studies' characteristics will be improved if they are presented separately for interventional and observational studies (particularly for the intervention type and expected study size). In this way, the table will provide more information about the studies' characteristics for each study type (interventional and observational). The benefits of publishing with F1000Research: Your article is published within days, with no editorial bias You can publish traditional articles, null/negative results, case reports, data notes and more The peer review process is transparent and collaborative Your article is indexed in PubMed after passing peer review Dedicated customer support at every stage For pre-submission enquiries, contact research@f1000.
4) Factorial assignment 9 (1. 5) Available 13 (1. 4) Crossover assignment 11 (1. 9) Masking (n=586) Stata 15. 0 (Stata Corp. , College Station, TX, USA) was used for the analysis of study characteristics. ResultsA total of 945 studies on COVID-19 have been registered in ClinicalTrials. gov up to April 29, 2020; 586 studies are interventional (62. 0%), and 435 of them (74. 2%) are randomized. Among interventional studies, the most frequent allocation scheme is the parallel group assignment (437; 74. 6%), followed by single group (111; 18. 9%], sequential (18; 3. 1%), factorial (9; 1. 5%), and cross-over assignment (11; 1. 9%). The majority of the clinical trials are open-label (no masking, 338 [57. 7%]); however, 57 (9. 7%) trials are double-blinded, 41 (7. 0%) triple-blinded, 90 (15. 4%) quadruple-blinded, and 60 (10.
The wavelength has been fixed to 0. 1 nm. For SAXS, data have been calculated in the q x and q y range from -1 to 1 nm -1 ; for WAXS, the range has been extended from -6 to 6 nm -1. Notice that, considering the Ewald sphere, the value of the q z depends on q x and q y , i. e. q z = k 0 cos sin -1 q x / k 0 cos tan -1 q y /q x -1. SAXS and WAXS maps, for the cases #1 and #2, are reported in Figs. 2  and 3 , panels D, top four images (SAXS), and panels E, top four images, (WAXS). Finally, we have calculated three-dimensional SAXS and WAXS maps of GSEs forming a nematic phase (Eq. 14), assuming that the director of the phase is oriented in the direction parallel or perpendicular to the X-ray or neutron beam ( xy and xz nematic views, respectively). In all calculations, the order parameter S has been fixed to 0.
Data availability The Fortran code developed to build the GSE and its scattering features is available upon request. The atomic coordinates used to construct the three-dimensional representations of A-type starch crystal structure, branching of double helices by α 1-6 linkage and the full atom representation of the content of the crystalline moiety of a platelet are available at polysac3db. cermav. cnrs. fr. Molecular graphics images displayed in Fig. 5a, b , were produced using the UCSF Chimera package from the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco (supported by NIH P41 RR-01081). Pettersen, E. F. , Goddard, T. D. , Huang, C. C. , Couch, G. S. , Greenblatt, D. M. , Meng, E. C. , and Ferrin, T. E. "UCSF Chimera-A Visualization System for Exploratory Research and Analysis.