dataesr/scientific-paragraphs-categorization huggingface dataset:
Seeds recorded the highest food group for Zn from Kenya at 56 mg kg -1. The total variance in crop Zn composition according to soil predictor variables was <10% for food groups, with the exception of leafy vegetables (20%) and roots\tubers (20%), with soil Al and S being the largest contributors to variance. Measured Zn supplies for food items originating from calcareous/non-calcareous soils and published composition data were not dissimilar 8. 9/9. 2/7. 6 mg capita -1 day -1 and 7. 8/9. 3/6. 9 mg capita -1 day -1 for Tanzania and Kenya, respectively, but for both countries measured intake values were higher than for published composition data. Zinc intake in this study is at the upper range for estimates by Schmidhuber et al. 4  worldwide database for Tanzania (6. 2-9. 3 mg capita -1 day -1 ), but at the lower range for Kenya (9.
4%) for fruit and soil Mn (7%) for root/tubers. Measured I supplies for food items originating from calcareous/non-calcareous soils and published composition data were not dissimilar 0. 029/0. 017/0. 030 and 0. 003/0. 024/0. 032 mg capita -1 day -1 for Tanzania and Kenya respectively. Measured supply data from Tanzania was comparable to using published composition data, yet for Kenyan measured values were significantly lower in calcareous soils compared to published values. The risk of I deficiency in both countries is significant (100%) according to the supply calculations for both countries, although drinking water sources and in particular, iodised salt intake are likely to alleviate the severe deficiency in food supply greatly. For example, 89% of the population of Africa would be at risk of I deficiency without iodised salt supplies, reducing the risk to 19% if iodised salt consumption is included in calculations, which contributed 63% of the total I supply in Africa.
This spatially resolved dataset will be invaluable to multidisciplinary efforts to investigate causal effects for human and animal health  70. For example, Schaafsma (2015)  71  identified an ecological association between estimated national level incidence rates of oesophageal cancer and predicted deficiency of dietary Se and Zn, pointing to a potential geochemical/spatial influence. This study uses data from several sources to supplement this new dataset, each with its own limitations. For example, food supply data are aggregated at national level and do not capture community level socioeconomic factors, seasonal variation in the type of food available or food wastage  3. Whilst the contribution of MNs to the EAR is small from drinking water, for severely deficient individuals this could still be a valuable intake route not considered in the food composition tables, particularly given that the consumption of 1.
Calculation of risk of deficiency. Food supply (g capita -1 day -1 ) data for each classified food item is summarised in Supplementary Table  5  for each country  66  , along with a correction for moisture content, showing the contribution of each food item to the overall dietary intake at a national level e. g. meat versus vegetables. For onward calculation of micronutrient supply, the daily food supply intake was multiplied by the MN concentration of the food item, as shown in Supplementary Tables  10  and 11  for Tanzania and Kenya, respectively, across three categories. These included for each food item, MN composition data originating from calcareous and non-calcareous soils in this study using food item data from Supplementary Tables  6  and 7  (food items) and 8 & 9 (food groups), compared to published MN data from FAO databases for Tanzania  9  and Kenya  60.
Acknowledgements Funding was received from the  BGS Global and BGS ODA  programmes, with additional support from the  Centre for Environmental Geochemistry Inorganic Geochemistry team. Acknowledgements  for additional assistance to this paper included: (1)  Fieldwork and laboratories in Africa -Louise Ander (BGS) ,  Timoth Ngowi ,  Leons Michael  (KCRI),  Esilaba Anabwani ,  Amimo Anabwani  (ESCCAPE),  David Samoie ,  Doreen Meso ,  Charles Owano ,  Jackson Masai  (UoE) and Public Health Officers from each county; (2) Laboratories-UK - Amanda Gardner ,  Tom Barlow ,  John Wheeler  (BGS).
Assessment of starch retrogradation and starch complexation by X ray diffraction The edge and center of the crackers are separated using a cookie cutter after a specific storage time. About 50 mg of each sample was sealed in a copper ring between two adhesive tape sheets to prevent any change in water content and examined by Wide Angle X-ray Scattering. Measurements were performed using a D8 Discover spectrometer from Bruker-AXS. Cu Kα1 radiation (Cu Kα1 = 1. 5405 Å), produced in a sealed tube at 40 kV and 40 mA, was selected and parallelized using a double Gobël mirror parallel optics system and collimated to produce a 500-µm beam diameter with sample alignment by microscopic video and laser. Data were monitored by a GADDS 2D detector for 10 minutes and normalized.
| Data collection We collected information on the household's socio-demographic and economic characteristics, as well as on food insecurity and dietary diversity indicators, among others. The sections of the questionnaire referring to household assets and food security were addressed to the head of the household. For the construction of the MDD-W an unquantified 24-h dietary recall was conducted in each household. The respondent of this recall was preferably the mother of a preschool-aged child, a mother, or a woman of the household; if none of these were available the head of household was interviewed. For the present study, only female respondents to the 24-h dietary recall were taken into account. The respondent was asked to list all the foods and drinks she had consumed the previous day and night and, with the help of the enumerator, to identify all eating or drinking occasions.
| Statistical analysis Data entry, including quality checks, was performed with Epidata software, version 3. 1. Data management was performed with SAS software version 8. 0, and data analysis were performed using STATA software, version 15. 0. The sampling design of the dataset was declared by defining enumeration areas as primary sampling units with the svyset STATA function. We used the svy STATA command to take into account the clusterization of the sample. As sample was selfweighted no sampling weights were applied. As the three surveys conducted were independent from each other (different samples selected each year) we were not able to analyse the data longitudinally, and thus we decided to pool the data of the three years in order to increase the statistical power of the analysis, and to test the potential impact of the year of data collection in the model by introducing interaction terms with the year of survey.
| DISCUSSION Overall 31% of women living in urban Burkina Faso consumed at least five food groups out of the ten predefined food groups to measure MDD-W (33% of WRA in Ouagadougou and 28% in Bobo-Dioulasso). In both cities the prevalence of MDD-W was significantly lower in the unstructured districts as compared with the structured ones. There is paucity of data from other urban settings to compare our what we observed in the structured districts of Ouagadougou but, surprisingly, 10 percentage points higher than in Bobo-Dioulasso. In two rural settings of Burkina Faso MDD-W post-harvest estimations (49% and 30%) were reported to be higher than the lean season estimates (26% and 23% respectively) within the same populations suggesting that seasonality has an impact on the indicator, at least in rural communities of this context  (Custodio, Kayitakire, & Thomas, 2015).
The fact that Ouagadougou showed higher MDD-W prevalence overall may be related to its condition of the capital city and thus markets may offer a wider range of food products. Furthermore, data was collected during the post-harvest season in Bobo-Dioulasso and in the beginning of the lean season in Ouagadougou, suggesting that the differences observed could have been starker if the data had been collected at the same time of the year in both cities. Indeed, in Ouagadougou a lower dietary diversity during the lean season was already reported by another study  (Becquey et al. , 2012). The overall low values of dietary diversity in our sample may be related to the effects of the food price crises which started in 2008  (Brinkman, de Pee, Sanogo, Subran, & Bloem, 2010) , as this type of crisis may especially affect urban households, as they depend on the market for food provisioning, and see their purchasing power reduced.
Nevertheless, the acquisition of this knowledge requires the establishment of a simple and rapid screening procedure. Current tools for the detection of change in size and shape of starch granules require the extraction and purification of the granules, followed by a particle counteranalysis  (Vandromme et al. , 2019)  or by sample observations under a light or electron microscope  (Roldán et al. , 2007; Lappe et al. , 2017). In addition, variations in the number of starch granules per plastid are generally evaluated from leaf sections observed under a light or electron microscope. This technique, however, depends on the section plane, and a large number of analyses must be carried out to obtain statistically reliable data  (Seung et al. , 2017). While these different techniques are suitable to characterize a specific mutant line, they are cumbersome, expensive, and ultimately incompatible for screening large collection of individuals.
Segmentation Automated analysis allows screening through a high number of images. Thus, we chose to analyze starch granules with the highest confidence level possible. Probability maps were first thresholded to an 80% confidence interval in each pixel for both starch granules and plastids. To reinforce the accuracy, we developed an ImageJ macro combining (i) various binary operations to ensure that detected starch granules were completely embedded into plastids, (ii) watershed operations to separate adjacent granules  (Beucher, 1992) , and (iii) automated particle detection, setting in ImageJ (1) "Set Measurements" to area, perimeter, and fit ellipses (from which diameter and major and minor axes are extracted) and (  2 ) applying the "analyze particles" module with in situ visualization of segmented starch granules.
Machine Learning for Automated Starch Granule Segmentation To characterize the starch granules in more details, one needs to measure their size and shape. However, manual segmentation is time-consuming and not adapted to analyze a large number of starch granules. While NegFluo provides ease to visually analyze images, extracting robust parameters to automatically discriminate between the different structures of the leaf is not trivial using parametric approaches. We thus developed an ML-based pipeline to circumvent this technical limitation. We developed the procedure based on Trainable Weka Segmentation module, since it combines the benefits of (i) FiJi  (Schindelin et al. , 2012)  for image processing and simplicity of the manipulation of microscopy files by biologists and (ii) WEKA for ML  (Frank et al.
, 2007; Crumpton-Taylor et al. , 2013). Furthermore, since the method was automated, a large number of granules could be measured, giving access to the global size distribution such as illustrated on the granules area in Figure  4B. In both Col-0 and ss4 lines, starch granule size distribution behaves like a Gaussian curve. The curve is shifted toward higher size in the ss4 line. To ensure the relevance of these measurements, it was required to compare morphological parameters extracted from NegFluo-ML to state-of-the-art starch analysis methods. We thus analyzed images from Col-0 and ss4 and compared them with both manual analysis of electron microscopy images (area, Figure  4C ) and extracted starch granules analyzed with a Coulter counter (diameter, Figure  4D ). For electron microscopy analyses, we used images previously acquired from TEM and manually determined starch granule size using ImageJ software.
Furthermore, human segmentation is often highly impacted by user-biased analysis. Indeed, humans have their individual ways to select objects boundaries or to discard objects that seems nonrepresentative. Thus, different experimenters cannot perform such studies with a good reproducibility, and NegFluo-ML overcomes this limitation. This manuscript focuses on the complete NegFluo/NegFluo-ML package applied to starch granule study in Arabidopsis leaf, but it can be extended. While both modules provide a complete acquisition and analysis pipeline, they are independent, and thus, NegFluo-ML can be transferred to other imaging methods such as SEM by adapting training features. Transposition to other plants could also be done after autofluorescence characterization adaptation. It thus opens the way to in situ, reliable, fast, and easy morphological characterization of starch granules.
ACKNOWLEDGMENTS The authors would like to thank  Ofilia Mvundza Ndjindji  ( CERMAV ) for the preparation of ultrathin sections,  Christine Lancelon-Pin  ( CERMAV ) for the ESEM observations, and the  NanoBio-ICMG Platform  ( FR 2607 ) for granting access to the electron microscopy facility. We are indebted to the  Research Federation FRABio (University of Lille, CNRS)  for providing the technical environment conducive to achieving this work. We thank  Agence Nationale de la Recherche  for funding (" CaSta-DivA"  Project, grant no. ANR-11-BSV6-0003 ).
Statistical analysis Individual rat was considered as experimental unit. The sample size was calculated for an expected difference of 20-25 % for corrected faecal protein digestibility between the control (casein) group and the gluten pasta diet group. A variance of 10 % was considered according to the previous studies  (13, 20)  for the selected parameter (corrected faecal protein digestibility). The type I error (α) was set at 5 %, the type II error (β) was set at 5 %, leading to 95 % power. All analyses were performed using Statistica 8. 0 software. All parameters were first analysed for normality. All data were tested by ANOVA with one factor: diet (one-way ANOVA), followed by a Fisher's least significant difference test to compare means at the 5 % significance level. Tendency towards statistical significance is considered if 0•05 < P < 0•1.
Acknowledgements The authors thank  GEMEF industries (Aix-en-Provence, France)  for kindly providing faba bean flour and  T. -M. Lasserre  and  J. Bonicel  ( UMR IATE, Montpellier, France ) for their technical assistance. This work was carried out with financial support from the  Auvergne regional council and  under the  VEGAGE  project that received financial support from ' Institut Carnot-Qualiment '. S. W. , V. M. , K. L. , J. S. , Y. B. conceived and designed the experiments. K. L. , A. B. , J. S. , V. P. , P. D. , C. G. , S. W. and V. M. performed the experiments. F. J. T. , A. G. and M. H. were involved in the analysis of CML. K. L. , V. M. and S. W. analysed the data and performed statistical analysis. K. L. wrote the paper. S. W. acquired the funding. The authors declare that there are no conflicts of interest.
After a loading and a washing-out period, slices were transferred into a home-made recording chamber under a 209 long-range water immersion objective (XLUMPLFL20XW, NA 0. 95) of an upright microscope (BX51WI, Olympus, Rungis, France), equipped with differential interference contrast and an infrared video camera. The chamber was continuously gravity perfused with oxygenated ACSF at a rate of 2. 5-3 mL/min except when slices were stimulated. Slices were stimulated for 15 seconds with a glass pipette (1MΩ) filled with 10 -4 M ATP or 500 ng/mL IL-17c under pressure. The pipette was placed at a distance of ~170µm from the apical part of the slice. For off-line analysis of calcium transients, images were background subtracted and changes in fluorescence were calculated relative to the averaged baseline (from 30 s before stimulation) and expressed as %∆F/F (((F-Fbaseline)/ Fbaseline)x100) using Xcellence RT software.
Statistical analysis Data are expressed as mean ± standard error of the mean (SEM). Regarding the immunohistochemistry quantification, the investigator performing the quantification was blind to the treatment group during analysis. Statistical significance was assessed using non-parametric Mann-Whitney tests or one-way or two-way ANOVA followed by Bonferroni multiple comparison post-hoc tests (GraphPad Prism software, GraphPad software Inc. , CA, USA). A probability value of P≤0. 05 was set for significant differences.
, 2009). As expected, caffeine elicited an increase in intracellular calcium in both OSNs and sustentacular cells independently of the presence of purinergic antagonists (Supplementary Data Figure  1 ). These results show that acute application of IL-17c is likely to induce the ATP release by olfactory mucosa cells, which in turn increase intracellular calcium level. We next explored if this process was still present in adult mice. As OM slices can only be performed on very young mice due to the solidity of the cranial bones, another technique was needed. We therefore used electro-olfactogram (EOG) recordings as a proxy to validate the functionality of the IL-17c system. EOG recordings reflect the responses of the OM to odorant stimulation  (Scott, and Scott-Johnson, 2002) , and ATP application on mice OM decreases odour sensitivity  (Hegg et al.
S5 ). The models were finally assessed by their predictive power to reprogram ORA5 into a ChA-responsive receptor. The binding poses of ChA in ORA5 and ORA6 were predicted to be very similar to LCA, which was expected given its nearly identical structure to the latter (Fig. S6 ). Without the 3␣-hydroxyl group, ChA fit better in the more hydrophobic pocket of ORA6 (Auto-Dock Vina score Ϫ10. 1 kcal⅐mol Ϫ1 , Haddock score Ϫ96. 8 Ϯ 2. 7) than in ORA5 (AutoDock Vina score Ϫ6. 7 kcal. mol Ϫ1 , Haddock score Ϫ68. 4 Ϯ 0. 6). Based on the models, we designed point mutations in the ORA5 pocket to optimize the interactions with the ligand. Four reprogrammed ORA5 mutants gained the function to recognize ChA in vitro (Fig. 5 ), suggesting the accuracy of the in silico models.
Ligand-binding pockets in ORAs are diverse and complementary with conserved patterns The site-directed mutagenesis data have verified the in silico models of the binding pocket in ORA5 and ORA6 (Fig. 6A ). We then assessed and compared the pocket properties in all the six ORAs. The pocket turned out to vary significantly in volume and hydrophobicity within the family (Fig. 6B ). The ORA5 pocket is the largest and the most hydrophilic. The one of ORA6 is the smallest and the most hydrophobic, consistent with its distinct ligand selectivity: high response to ChA (small and hydrophobic) and no response to taurolithocholic acid or glycolithocholic acid (both large and hydrophilic). ORA1 and ORA2 showed most similarities in the pocket, in terms of sequence (62% identity) and physiochemical properties (Fig.
The assay was carried out as follows: 24 h after transfection, medium was replaced with 100 l of odorant solution (at different doses) diluted in Opti-MEM (Thermo Fisher), and cells were further incubated for 4 h at 37 °C and 5% CO 2. After incubation in lysis buffer for 15 min, 20 l of Dual-Glo™ Luciferase Reagent was added to each well of 96-well plate and firefly luciferase luminescence was measured. Next, 20 l Stop-Glo Luciferase Reagent was added to each well and Renilla luciferase luminescence was measured. Data analysis followed the published procedure in Ref. 23. Dose-response curves were fitted by GraphPad Prism with the four-parameter logistic regression. Hana3A cells, GFP, RTP1S, CRE-Luciferase, and SV40-Renilla constructs were kind gifts from Dr. Hiroaki Matsunami (Duke University).
Molecular modeling Homology modeling and docking-The HHpred webserver (30) was used to determine the structure template and the sequence alignments. We built 100 models for each ORA with Modeler v9. 15 (31) using the crystal structure of human angiotensin II receptor type 1 (PDB ID 4ZUD) (32) as template. MDpocket  (33)  was used to statistically assess the pocket properties using the 100 models. For docking, we chose the model with the lowest DOPE score for each ORA. AutoDock Vina  (34)  and the Haddock 2. 2 webserver  (35)  were used to identify a common top-ranked binding pose. Residues in the putative ligand-binding pocket were set flexible during docking.
Edited by Henrik G. Dohlman This work was supported by  National Natural Science Foundation of China  Grants  81700894  and  31771155  (to Y. Y. );  Shanghai Municipal Education Commission , the  Shanghai Eastern Scholar Program  (to Y. Y. );  Shanghai Municipal Human Resources and Social Security Bureau ,  Shanghai Talent Development Fund  (to Y. Y. ); the  EENT Hospital of Fudan University, the Startup Fund  (to Y. Y. );  Shanghai University, the Startup Fund  (to Y. Y. ); the  French National Research Agency  ( Agence Nationale de la Recherche, ANR ), Project  NEUROLF  (to J. G. ) as part of the  ANR-NSF-NIH Collaborative Research in Computational Neuroscience ; the  French government , through the  UCAJEDI Investments in the Future project managed by the ANR  Grant  ANR-15-IDEX-01  (to X.
Covalent immobilization of β-casein on CM5 sensor chip β-casein was covalently immobilized on the CM5 sensor chip by a standard aminecoupling chemistry according to the manufacturer's protocol (GE Healthcare). Briefly, the surface was functionalized with 0. 4 M EDC and 0. 1 M NHS mixture. 40 µM βcasein stock solution was prepared in HBS-EP buffer (pH 7. 4). The ß-casein monomeric state in the experimental conditions was checked by dynamic light scattering analysis (data not shown). The solution was vortexed and placed in an ultrasonic bath for 10 min. Prior to covalent immobilization, a preconcentration test at different pH values was performed to obtain the optimum pH for the immobilization procedure. Thus, β-casein stock solution was diluted at 20-30 µg/mL in 10 mM acetate buffer at pH 3.
The signal of the control surface flow cell was subtracted from the signal of the ß-casein flow cells. Chloramphenicol, a negative control, was also injected at 500 and 1000 µM in HBS-EP+ containing 3% (v/v) DMSO. Each experiment series included blanks (running buffer), injection of bioactive compound, negative control, and solvent corrections. Experiments were repeated three times. Sensorgrams were analyzed using BIA evaluation 3. 0 software by steady-state fitting with the one-to-one Langmuir binding model to obtain KD value. When a bioactive compound, injected in the flow system as the analyte, reacts with ß-casein, immobilized as the ligand, to form a complex, the rate of the complex formation depends on the free concentrations of the bioactive compound ([Bioactive]) and ß-casein ([ß-casein]) as well as the stability of the formed complex ([Bioactive-ß-casein]) and can be described by the Langmuir binding model equation (Eq.
Background: Colorectal cancer (CRC) is a highly devastating cancer. Ca 2+ -dependent channels are now considered key regulators of tumor progression. In this study, we aimed to investigate the association of non-voltage gated Ca 2+ channels and Ca 2+ -dependent potassium channels (KCa) with CRC using the transcriptional profile of their genes. Methods: We selected a total of 35 genes covering KCa channels KCNN1-4, KCNMA1 and their subunits KCNMB1-4, endoplasmic reticulum (ER) calcium sensors STIM1 and STIM2, Ca 2+ channels ORAI1-3 and the family of cation channels TRP (TRPC1-7, TRPA1, TRPV1/2,4-6 and TRPM1-8). We analyzed their expression in two public CRC datasets from The Cancer Genome Atlas (TCGA) and GSE39582. Results: KCNN4 and TRPM2 were induced while KCNMA1 and TRPM6 were downregulated in tumor tissues comparing to normal tissues.