dataesr/scientific-paragraphs-categorization huggingface dataset:
In short, the sensitivity of the daily increment of leaf area index and yield to each of five different stress factors (N deficit, heat, cold, water deficit and water excess) was determined using the relationship among the yield gap (the ratio between actual and potential yields) in 2000, knowledge stock in 2000 and simulated average growing season stress factor in 1996-2005. The crop coefficient values for millet and sorghum used in this study are available in Table  S1. The CYGMA model operates globally for millet and sorghum, similar to the four crops previously modelled; therefore, the simulated yields in West Africa were used for this study. Historical, factual and counterfactual crop simulations. The crop model simulation experiment has two purposes (Table  3 ). First, climate observation data are used to force the crop models to compare simulated crop yields with observed crop yields and check the performance of SARRA-H and CYGMA on the historical period.
The two crop models are run using the same historical forcing dataset S14FD in West Africa (20°W to 15°E; 4°N to 18°N) for the period 1961-2012. Second, factual and counterfactual climate simulations are used to force the crop models to reproduce the crop yields under climate with (factual) and without (counterfactual) human influences. The comparison between the factual and counterfactual crop yield simulations enables the quantification of the effect of human influence on historical crop yields as simulated by the two crop models. The SARRA-H simulations include four sets of parameters to capture the diversity of crop management in West Africa. They aim to reproduce the behaviour of two representative varieties of millet and two varieties of sorghum that differ mainly by the length of the growth cycle-a short duration crop (approximately 90 days) and a long duration crop (approximately 120 days) that are both insensitive to the photoperiod -and two levels of fertilization rate by modifying the biomass conversion ratio to a sub-optimal level and a better level to simulate, respectively, a low and a moderate fertilization rate.
These socioeconomic data were obtained from the World Bank and FAOSTAT databases. The sowing dates of the individual crops in 2000 in the CYGMA model follow those reported in the Monthly Irrigated and Rainfed Crop Areas around the year 2000 (MIRCA2000) dataset  29  but change dynamically according to the long-term temperature and moisture conditions calculated in the model. The extent of harvested area and the fractions of irrigated and rainfed areas in 2000 were obtained from the M3-Crops dataset and MIRCA2000 dataset, respectively. We also used the Historical Irrigation Information (HID) dataset  30  to take the annual growth rates of irrigation-equipped area (if any) into account. However, in West Africa, there are almost no irrigated areas. Therefore, the crop yields in West Africa simulated by the CYGMA model represent those in rainfed conditions.
Factual and non-warming counterfactual crop simulations were performed using the two crop models with the 100 factual and counterfactual ensemble climate simulations as the inputs to the crop models to estimate the impacts of historical climate change on crop yields. The climatic variables used as the model inputs include daily mean, maximum and minimum 2-m air temperatures, precipitation, downward shortwave radiation flux, relative humidity and 10-m wind speed. The ET 0 estimation is calculated by the Penman-Monteith FAO56 equation  31  in the SARRA-H model and a variant of this equation in the CYGMA model  6. production losses associated with historical climate change. For a given year, country and crop, we first calculated the yield impacts at a grid-cell level by comparing the historical and non-warming counterfactual crop simulations: ∆ = - ( ) Y Y Y Y / , (1) t g historical t g counterfactual t g historical g , , , , , , 2000:2009, where suffix t and g indicate the year and grid cell, respectively; ΔY is the simulated grid-cell yield impact (ratio); Y historical is the simulated grid-cell yield under the factual climate condition (t ha -1 ); Y counterfactual is the simulated grid-cell yield under the counterfactual climate condition (t ha -1 ); and Y historical, 2000:2009 is the simulated grid-cell average yield over the baseline period (2000-2009) calculated using factual crop simulation data (t ha -1 ).
The calculated grid-cell yield impacts in a country were aggregated: ∆ = ∑ ∆ × ∑ = = Y Y A A , ( 2 ) t c g G t g M Crops g g G M Crops g , 1 , 3 ,2000, 1 3 , 2000, where suffix c indicates the country, and A M3Crops 2000 is the reported grid-cell harvested area in 2000 from the M3Crops dataset (ha). To avoid unrealistically large yield impacts, which could numerically occur when the simulated yield under the factual climate condition is low (e. g. , <0. 1 t ha -1 ), we replaced ΔY t , c with -100% when ΔY t , c was below -100%. In contrast, ΔY t , c was replaced with +100% when it was greater than +100%. Finally, the production impact was calculated as follows: where ΔP is the impact on annual production of a crop of interest on a country level (USD); ΔY is the simulated country annual yield impact relative to the simulated average factual yield in the baseline period (ratio); Y FAO,   2000:2009 is the FAO-reported country average yield in the baseline period (t ha -1 ); A M3Crops , 2000 is the reported country harvested area in 2000 calculated using M3Crops dataset (ha); and P FAO, 2000:2009 is the FAO-reported country average producer prices in the baseline period (USD t -1 ).
In the FAO database, no effective harvested area data were available for some years and countries in West Africa. Therefore, while time-constant, we used the grid-cell harvested area data obtained from the M3Crops dataset instead of FAO-reported data. Different price data (i. e. , consumer price) were available in the FAO database, but consumer prices are strongly affected by   [13] [14] [15]  Factual crop yield simulation Bias-corrected MRI-AGCM3. 2 simulations without human influence on climate systems  [13] [14] [15]  287 ppm (the level of the year 1850) Δ Δ = × × × P Y Y A P , ( 3 ) t c t c FAO c M Crops c F AO c , , , Counterfactual crop yield simulation Table  3. The crop simulation design. variations in oil price and export bans in major food-exporting countries. Therefore, we used producer prices to minimize the impacts of these confounding factors on our estimates of crop production losses.
Results impacts of historical human activities on the regional climate of West Africa. Human influence has strongly modified the annual mean temperature in West Africa, with a regional warming depicted since the 1950s (Fig. 1a ). We found a difference of approximately 1 °C when comparing the average temperature of the last decade 2000-2009 between the factual and counterfactual climate simulations (Table  4 ). Although this warming is simulated all over West Africa, it is more pronounced in the north and central Sahel and in the Sahara (Fig. S1a ). This is certainly the most robust change, with all statistical tests showing significant values (Table  4 ). In addition, the factual climate simulation, which includes human influence, is able to more accurately reproduce the mean and the variability of the observed annual temperatures derived from the CRU dataset  32  than the counterfactual climate simulation (Fig.
However, more significant changes are detected for rainfall intensity and in heavy rainfall in the Sahel over the last decade, which can be deemed the impacts of human activities (Table  4 ; Fig. S1 ). This is particularly clear when we consider the number of rainy days exceeding 50 mm/day (Fig. 1d ), where we found a significant increase of 27. 7% in the last decades between the factual and the counterfactual simulations. performance of crop models to simulate historical yields. The reported data for West Africa obtained from the M3-Crops dataset showed that the reported millet yields were almost the same as the sorghum yields (Fig. 2 ). The yield dataset is solely based on reported yield statistics and represents the average yield circa the year 2000 (that is, the average in 1997-2003). However, the different crop models reproduced the reported data differently.
These results suggested that the models evaluated here, although imperfect, had a certain usefulness and complementarity in assessing the climate impacts on yield variability and trends. The CYGMA model accurately reproduces the yield trends, while the SARRA-H model better reproduces year-to-year variability. However, the comparison between reported and simulated country yields in West Africa is difficult partly because -although FAO yield statistics are the best reliable source of information -many data values from the FAO database in the region are derived from data imputation or unofficial data sources (Table  S2 ), and historical management information is not available. the impacts on crop production due to historical climate change. The simulated impacts on average yields in 2000-2009 due to historical climate change varied by location and between the two crop models (Fig.
This production loss is mainly driven by the temperature change between the factual and the counterfactual climate simulations, whose spatial pattern (Fig. 7 ) is similar to the pattern of yield change (Fig. 5 ); the highest production losses are located in the northern Sahel, where the warming is more intense. The relationship between temperature change and yield change is particularly clear when the SARRA-H model is used (Fig. 8 ). A negative Figure  2. Geographical patterns of the reported and simulated average yields of millet and sorghum circa in 2000 across West Africa. The reported data were obtained from the M3Crops dataset  26. Both crop models SARRA-H and CYGMA used the same forcing dataset S14FD. For SARRA-H outputs, only locations where the individual crops were harvested were shown.
S6 ) and in SARRA-H in a previous study  9  , in addition to shortened crop duration with increased temperatures. However, CYGMA-based yield and production losses are almost always smaller than the Figure 3. Comparison of annual yield time series reported by the FAO and the average yields over West Africa simulated by SARRA-H and CYGMA for millet and sorghum for the period 1961-2012. The regional average data for West Africa were calculated by averaging the data over Benin, Burkina Faso, the Gambia, Guinea, Guinea Bissau, Mali, Niger, Nigeria, Senegal, Sierra Leone and Togo (country harvested areas were used as the weights). The grid-cell harvested area in 2000 was used as the weight when computing country average yields. Three different metrics were used: 1) the reported and simulated raw yield data were compared without any post-processing (raw); 2) the reported and simulated annual yield data were separately scaled so that each average yield in 1996-2005 was equal to one, and then the data were compared (scaled); and 3) the percentage yield anomalies, relative to the normal yield calculated as the 5-yr running average, were separately computed for the reported and simulated data and then compared (anomaly).
Although no single best crop model was found, the CYGMA model showed its relative advantage in capturing the yield growth driven   to 75% (box) and 5 to 95% (vertical line) confidence intervals derived from ensemble members of the crop simulation. The horizontal lines indicate the median. The regional average data for West Africa were calculated by averaging the data over Benin, Burkina Faso, the Gambia, Guinea, Guinea Bissau, Mali, Niger, Nigeria, Senegal, Sierra Leone and Togo. The range of simulated yield change was limited to the interval from -100% to +100% to avoid unrealistically large yield impacts in locations where the current yield is very low. by technological improvements. In contrast, the SARRA-H model showed its relative advantage in capturing yield anomalies, which are mainly driven by seasonal climate variability.
7  and Guan et al. 8  , and a review can be found in Sultan and Gaetani  2. Every modelling study has its limitations, and we recognize a few caveats to the design of our simulation experiments. Caution is necessary when interpreting the crop simulation results presented here because the quality of the reported data in the studied region is not as high as that in developed countries, although the FAO statistical database used here is the most reliable, accessible source of information on crop yields. The differences between the reported and simulated data described earlier are mainly due to imperfect modelling and errors in model inputs (this is especially the case for the socioeconomic variables used as the inputs to the CYGMA model) and the assumptions used in the model setup (e. g. , the assumptions on management levels used in the SARRA-H model).
However, errors in reporting, inadequate sampling in census surveys and data imputation could be other reasons for the differences. Indeed, a notable percentage of the data in the studied region is based on the data imputation at the FAO, with unofficial or unavailable sources (Table  S2 ). For this reason, we used the harvested area data from 2000 obtained from the M3-Crops dataset throughout the studied period. However, FAO data show that the harvested areas of these crops change over time (14. 6 M ha to 13. 7 M ha for millet and 12. 0 M ha to 13. 8 M ha for sorghum for the period 2000-2016), which may lead to different production loss estimates. However, crop-specific gridded historical harvested area data are not available. In conclusion, our study demonstrates that human activities have already had a significant impact on the regional climate in the Sahel, with negative consequences for the production of major cereals in the region.
Figure6. Estimated impacts on the average annual yield and average production of millet and sorghum in 2000-2009 for selected major crop-producing countries in West Africa. The impacts were measured as the difference between the factual and counterfactual crop simulations. Harvested area weighting was considered when computing country and West Africa average yield impacts. Box plots indicate the mean (cross), with 25 to 75% (box) and 5 to 95% (vertical line) confidence intervals derived from ensemble members of the crop simulation. The horizontal lines indicate the median. The regional average data for West Africa were calculated by averaging the data over Benin, Burkina Faso, the Gambia, Guinea, Guinea Bissau, Mali, Niger, Nigeria, Senegal, Sierra Leone and Togo. The range of simulated yield change was limited to the interval from -100% to +100% to avoid unrealistically large yield impacts in locations where the current yield is very low.
Acknowledgements This study was supported by the  FY2017 SAKURA Researcher Exchange Program between JSPS  and MAEDI. T. I. was supported by the  Environment Research and Technology Development Fund  ( S-14 ) of the  Environmental Restoration and Conservation Agency, Japan. D. D. was supported by the  NERC/DFID   Future Climate For Africa programme  under the  AMMA-2050  project, grant number  NE/M020126/1. This study utilizes the d4PDF database, which was produced under the SOUSEI programme sponsored by the  Ministry of Education, Culture, Sports, Science and Technology of Japan. The CYGMA crop model simulations were conducted on the  High-Performance Cluster Computing System of the Agriculture, Forestry and Fisheries Research Information Technology Center ,  Ministry of Agriculture, Forestry and Fisheries of Japan.
Structural characterizations of the ZnO seed layer and NWs were performed using X-ray diffraction (XRD) with a CuK1 radiation on the high-resolution parallel beam diffractometer (Bruker AXS D8 discover, Karlsruhe, Germary). The morphological characterizations of the ZnO NW surfaces Structural characterizations of the ZnO seed layer and NWs were performed using X-ray diffraction (XRD) with a CuK1 radiation on the high-resolution parallel beam diffractometer (Bruker AXS D8 discover, Karlsruhe, Germary). The morphological characterizations of the ZnO NW surfaces and cross sections were done by high resolution transmission scanning electron microscope (HRSEM, JEOL JSM-7900 F, Croissy-sur-Seine, France). The extraction of the densities, lengths, and diameters associated with the ZnO NWs was obtained using Image J software on SEM images.
These results confirm that the characteristics of the ZnO NWs are well-shaped c-axis orientation. These characteristics improved due to the use of an annealed ZnO thin seed layer, which was confirmed by SEM images shown in the following figures. The surface morphology of ZnO NWs grown on non-annealed and annealed ZnO seed layers with different thicknesses is shown in Figure  4a ,b, respectively. It is clear that the NWs growth was Chemosensors 2019, 7, 7 7 of 13 strongly influenced by the properties of ZnO seed layers. The NWs with a non-annealed seed layer had different densities (Table  2 ), which were estimated by counting the number of NWs from such SEM images using "Image J" software, as mentioned in the experimental section. The NWs density increased significantly with the seed layer thickness decrease, from 35 NWs/µm 2 for the 100 nm sample to nearly 54 NWs/µm 2 for the 5 nm sample, which can be justified by the change in crystal size depending on the seed layer thickness.
Funding:  EnSO  project has been accepted for funding within the Electronic Components and  Systems For European Leadership Joint Undertaking  in collaboration with the European Union's  H2020 Framework Programme  ( H2020/2014-2020 ) and  National Authorities  [Grant agreement No. 692482 ]. This work was also supported by the  French National Research Agency  [Grant agreement  ANR-14-CE08-0010-01 ]; and the  Region Centre  [Grant agreements  2014-00091629  and  2016-00108356 ].
They also discussed the 10 elements that must be covered in a lay summary listed in Annex V of the regulation. Lay summaries address the general public as well as participating subjects and patients. A summary must be prepared for every clinical trial and be posted on the EU Portal within 12 months after the end of the study. For phase I trials without therapeutic intent, this timeline may be extended up to 30 months. Shorter timelines apply for pediatric trials (6 months). Available guidance documents regarding structure and content as well as literacy and numeracy principles applicable for a lay summary were addressed. An example summary of a typical pharmacokinetic trial in healthy subjects was presented. Questions from the audience concerned the description of primary and secondary endpoints, the sociocultural aspects of data presentation, wording of safety aspects and formatting.
He noted that science and research are international, and any trial data will be accepted in EU marketing authorization applications so long as they meet scientific and ethical standards equivalent to those that apply within the EU. The EU is also working on the renovation of ICH GCP, to prepare the regulatory landscape for future medicines, future trial designs, and future data sources. The emphasis is on the role of achieving quality through good design. Revisions in the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use-General Considerations for Clinical Trials (E8), and Guideline for Good Clinical Practice (E6) are expected to proceed from 2019 to 2026, respectively. The aim is to support innovative approaches to clinical trials including: facilitating a broad range of study designs and data sources; upfront assessment of risks specific to a study design, protocol and procedures, proportionate risk management and controls focusing on critical study elements, and use of technological tools to ensure robust conduct, oversight, and reporting.
Virginia Parks (Takeda, USA) provided an industry insight into current perspectives on digital biomarker development in early clinical research. She detailed how wearable technology is being increasingly implemented in clinical drug development-data from clinicaltrials. gov identified 330 clinical trials that included their use in 2017. Technology allows the collection of observable data non-invasively, in real-time, in a patient's natural setting to enhance our understanding of the effects of treatment and how symptoms may change over time. Combined with predictive analytical techniques such as machine learning, wearable technologies promise to contribute to the advancement of innovative therapeutics as effective treatment solutions through novel endpoint, or digital biomarker discovery. Although there is much enthusiasm for its use in medical research, more rigorously gathered data is needed for the field to progress and to establish valid methodologies.
Results Single-cell analysis reveals heterogeneity in large wounds. We performed scRNA-seq on unsorted cells from wound dermis 12 days post-wounding (PW) (Fig. 1a ). This time point coincides with completion of wound re-epithelialization and strong SMA expression  5. Approximately 21,819 sequenced cells met quality control metrics (Supplementary Figure  2 ) and were analyzed. Unsupervised clustering using the Seurat package  25  identified 13 cell clusters (Fig. 1b, left ). Using the differentially expressed gene signatures, we attributed clusters to their putative identities (Fig. 1b , right) and hierarchical similarities (Fig. 1c ; Supplementary Figure  3a ). Figure  1d  provides a summary diagram of identified cell types. Figure  1e -g show selected differentially expressed genes in the form of a heatmap (Fig.
We also examined pseudotime dynamics using scEpath  33  , which identifies pseudotime-dependent genes and arranges them into clusters. This analysis revealed five gene clusters (Fig. 3c ), including multiple signaling factors and TFs (Fig. 3f, g ; Supplementary Figure  10 ). Overlaying RNA velocity vectors onto these clusters suggests that cluster pC1 contains genes expressed at the beginning points of Path1 and Path2, cluster pC2 primarily contains Path1 differentiation program genes, cluster pC3-mainly Path2 differentiation program genes, whereas clusters pC4 and pC5 are largely composed of Path3 differentiation program genes. Taken together, pseudotime, RNA velocity, and scEpath analyses establish a basis for exploring signaling and transcriptional regulators of wound myofibroblast differentiation programs.
4j ). Importantly, Path3 vectors, which encompass the overlap region, appear to represent putative conversion of Lyz2 + myeloid cells into fibroblasts (red on Fig. 4j ), while Path4 vectors appear to mark differentiation of woundresident myeloid cells (green on Fig. 4j ). Overlaying RNA velocity vectors onto scEpath data indicates that genes in cluster rC5 mark myeloid differentiation program along Path4, while genes in clusters rC4 and rC3 mark early and late stages of putative myeloid-to-fibroblast conversion pathway, respectively. f Igfbp4 C1qtnf3 Cxcl2 Ccl7 Igfbp6 Angptl1 Sfrp1 Angptl4 Ltbp3 Igf1 Fstl1 Nenf Igfbp5 Igfbp3 C1qtnf6 Spp1 Sfrp2 Ccl8 Igsf10 Mydgf Mif Tgfb2 Tgfb1 Il6 Igfbp7 Ccl2 Cxcl1 Pdgfa Dlk1 Signaling factors Pseudotime Pdgfa +3 -3 +3 -3 Pseudotime Pseudotime Ogn Klf4 Nfix Klf2 Nfkb1 Nupr1 Atf3 Nfia Nfatc1 Zfp36 Klf9 Foxp1 Nfat5 Klf6 Sox4 Lmo4 Twist2 Cbx3 Cbx1 Kdm6b Twist1 Kdm5b Stat3 Xbp1 Arid5b Ctnnb1 Klf13 Tcf12 Zfp91 Edf1 Ebf1 Id3 Nfkbia Zeb2 Bach1 Nr2f2 Mef2c Next, we performed scRNA-seq on genetically marked contractile cells, isolated from day 12 wounds of Sm22-Cre; tdTomato mice (Fig.