dataesr/scientific-paragraphs-categorization huggingface dataset:
Cheng et al. (2015) exploit this idea to identify trades institutional investors make for their own account. Specifically, they study the joint responses of traders' positions and commodity futures prices to changes in the VIX using the CFTC's LTRS database, where the VIX is a proxy for shocks to financial institutions' risk appetite during the crisis (see, for instance, Brunnermeier et al. , 2009;Coffey et al. , 2009). They find that, during the crisis, aggregate positions of hedge funds and CIT fell in response to an increase in the VIX in a large number of commodity futures markets. On the opposite, hedgers seem to take the other side of those trades as their net positions display a positive response to changes in the VIX. It means that, during the crisis, hedgers reduced their net short hedging positions just as uncertainty was rising.
Data We consider a total of 18 commodities: 12 agricultural commodities (Chicago wheat, Corn, Kansas City wheat, Soybeans, Soybean oil, Feeder cattle, Lean hogs, Live cattle, Cocoa, Coffee, Cotton, Sugar), 3 energy commodities (WTI crude oil, Heating oil, Natural gas), and 3 metals (Copper, Silver, Gold). Because our analysis relies on open interest data from the CFTC's reports -which are released on Fridays for closing positions of the preceding Tuesdays -, we focus on the weekly frequency. It should be pointed out that weekly open interest can hide traded volume several times higher than the resulting positions, and, in that regard, data availability is a considerable limit to our analysis. Our dataset starts on June 20 th 2006, when CIT positions first become available, and ends on September 24 th 2019 (685 points).
They are either hedge funds, commodity trading advisors (CTAs), or commodity pool operators (CPOs). And, finally, Other Reportables designate non-commercial traders who did not belong in any other category. Several papers assess the effect of index traders on prices using the Swap Dealers category. Index investments in commodity futures markets is indeed primarily made through swaps (Sanders and Irwin, 2011a), but not exclusively, and the remaining index positions are reported either in the Managed Money or the Other Reportables categories. Moreover, an arguably more important issue that hinders the Swap Dealers category from being a reliable proxy for CIT positions stems from the fact that it includes non-indexed over-the-counter (OTC) positions. That is, Swap Dealers data mingle positions related to index investing with positions related to physical commercial activity.
It relies on the composition of the two most popular commodity indices (the S&P GSCI and the Bloomberg Commodity Index) and CIT data for agricultural commodities to infer exposure from index investors into non-agricultural commodities. However, we found this approach to be very sensitive to small changes in the index composition, and we eventually decided to adopt a simpler but more transparent method proposed by Cheng et al. (2015). 13 For non-agricultural commodities, our proxy for changes in positions related to index investing is therefore: pos agg;CIT t ¼ P 12 i¼1 pos i;CIT t. 14 We also compute weekly change from Tuesday to Tuesday for our control variables. Whenever the Tuesday value is missing, we compute our weekly change by using the Monday value. Whenever both Monday and Tuesday values are missing, we used the value on the day closest to Tuesday.
This procedure to handle missing values was necessary only for a handful of data points. (iii) The weekly change in the Baa credit spread. Changes in the Baa credit spead reflect evolving credit conditions in the economy. It is computed as the difference between Moody's seasoned Baa corporate bond yield and the yield on 10-year Treasury with constant maturity. The lower the spread, the more favourable credit conditions are, and the higher expected future demand for commodities. We retrieve the Baa credit spread from the Fred database. For Chicago wheat, Kansas City wheat, Corn, Soybeans, Soybean oil, Cotton, and Sugar, we adopt three additional control variables available at a monthly frequency in the U. S. Department of Agriculture's (USDA) World Agricultural Supply and Demand Estimates reports.
Our results to the methodology (borrowed to Cheng et al. , 2015) that consists in conditioning financial traders' positions on shocks to the VIX are overall mitigated. The method might give different results if it were applied to a more accurate dataset such as the LTRS. Data availability, and the fact that the CFTC necessarily classifies traders by industry affiliation, while economists refer to different trading strategies or behaviours, remain important impediments to the identification of traders' impact on prices. Financialization has already attracted considerable attention. Yet, given how dynamics in futures markets can spread to spot prices (Sockin and Xiong, 2015;Henderson et al. , 2015) and along global commodity chains (Staritz et al. , 2018), we feel like measuring how speculation impact commodity prices is still a crucial topic on the research agenda.
Genotype covariances based on either a linear kernel (GxE GBLUP) or a Gaussian kernel (GxE RKHS) have been tested, and the Gaussian kernel allows a more flexible structure than the linear kernel and potentially better prediction  (Cuevas et al. 2016). To optimize calibration with the multi-environment data, various strategies of genome-based models including GxE were proposed  (Jarquı ´n et al. 2020). The authors compared different partitioning of the calibration sets among the multiple sites where the population was tested, with different degree of overlapping of the genotypes between environments. Sparse testing designs in which subset of the genotypes are tested in each location was presented as a method to reduce the experimental effort and optimize the use of breeding program resources.
The YLD value was neither adjusted for the plot size nor for the count of fertile plants. The grain zinc concentration (ZN), expressed in parts per million, was measured on a subsample of collected grains polished in Teflon equipment, using energy dispersive X-ray fluorescence spectrometry (X-supreme 8000, Oxford Instrument, Shanghai, CN) available at the CIAT-HQ Nutritional Laboratory. The exact same procedure was used for generation S 0:2 in 2017 and generation S 0:3 in 2018. The 50 temporal checks were phenotyped as S 0:2 in 2017 and as S 0:2 and S 0:3 in 2018. This allowed measurement of the nonconfounded year within site effect on the S 0:2 and the generation effect in 2018 by analyzing the data from the S 0:2 and S 0:3 lines as presented in Appendix B.
Effect of sites and generations on the phenotypic performance The phenotypic data were collected in two sites and on the same S 0 progeny at two generations. In each site, the phenotyping was done in 2017 for 334 families at the S 0:2 generation and in 2018 for the same 334 families at the S 0:3 generation. For most traits recorded in the two locations, the mean phenotypic values differed between sites (Table  1  and Figure   3 ). Although the differences between sites were moderate for FL and PH, they were large for YLD and ZN, with more than 60% change in the 2017 trials. The S 0:2 families evaluated in 2017 had later flowering, shorter PH, lower yield and higher zinc concentration in SRO than in PAL. However, this tendency did not hold for the 2018 trials. The differences between sites in PH were greater at the 2018 trials, with taller S 0:3 plants in SRO.
Optimization of calibration procedure for GP In our study, we tested the calibration of a GP model using phenotypic records gathered from early progeny testing in two sites. The potential of using two-site data and sparse testing for the model calibration, was considered as a satisfactory measure to predict most traits, even for YLD, despite a slightly reduced advantage compared with what was reported for the other traits. We have demonstrated that the calibration using phenotypic data collected on progeny testing at two successive early generations could deliver relatively good and comparable PA. This opens up possibilities for rapid cycling RS, with recycling of parental lines from the genotyping of S 0 plants, based on the breeding value of the S 0. Yet, there is still a need to confirm that the models do predict well the performance of more advanced generations for inbred line development.
2017; Ramasubramanian and Beavis 2020)  and showed that the persistency of PA across cycles could be achieved with the accumulation of data from several past cycles. Simulation studies will be performed on our population to optimize the long-term use of GP-aided RS and define how and when it is best to upgrade the calibration model. The simulation will also offer the opportunity to improve the prediction and apply genomic selection while maintaining enough genetic diversity for further use of the population. Cedric  Baertschi et al. | 11.
Acknowledgments The authors would like to thank all the scientists, field workers, lab assistants from  Alliance Bioversity-CIAT  who have contributed to the data collection. Special thanks go to  Joe Tohme  and  Maria Fernanda Alvarez  for their support. Additional thanks are due to the  FLAR Grain Quality Laboratory , the  HP-CIAT Nutritional Laboratory  for grain quality evaluation and to Fedearroz for the access to field facilities at their research station in  Santa Rosa.
The study showed that the drought over the  1907-1916 (1913)  was the most devastating among the three studied droughts. In the same way, the study of  Nicholson [9]  on the description of the rainfall fluctuations in Subtropical West Africa over the period 1901-1973, determined a significant decrease in the annual rainfall over the three periods  (around 1913, around 1940 and from 1968 to 1973). Furthermore, L'hote et al. [13]  analyzed the trends in the Sahelian rainfall over 1896-2000 period from 21 synoptic stations well scattered from Senegal in the West to Chad in the East. They computed the annual rainfall index with regard to a reference period of 80 years (from 1921 to 2000) determined on the basis of data availability at21 stations. Their results showed four main stationary periods: a deficit over 1910 to 1916 (7 years), an excess over 1950 to 1967 (18 years), a deficit over 1970 to 1974 (5 years) and a deficit over 1976 to 1993  (18 years ).
Study Area and Datasets West African Sudano-Sahelian zone is characterized by a monomodal climate with one rainy season and one dry season. The rainy season occurred over the period from May to October of each year and the dry season covers the other months. The study area considered is defined by the square delineated in the south by the latitude 9. 0 • N and 17. 5 • N in the north and in the west by the longitude 22. 5 • W and the longitude 16. 0 • E (Figure  1 ). This zone concerns 13 countries in West Africa (Cote d'Ivoire, Benin, Burkina Faso, Ghana, Guinea Conakry, Guinea Bissau, Gambia, Mali, Niger, Nigeria, Mali, Mauritania, and Togo). The heterogeneous climate area of Sudano-Sahel is covered by 1188 meshes (corresponding to 1188 grid points) at the spatial resolution of 0. 5 • longitude × 0.
5 • latitude. The pattern of the annual rainfall is a north-to-south gradient of 1 mm/km  [11]. The annual rainfall amount varies from about 100 mm at 17 • N to about 1200 mm at 9 • N (averages computed over 1971-2000), with a slight decrease from West to East along the same latitude. (NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www. esrl. noaa. gov/psd/, accessed on 6 May 2021). The gridded data are thereafter designed as from the last year: CRU_1995, CRU_2002, CRU_2009, CRU_2012, IRD_1998, and UDEL_2017. It can be emphasized here that the importance of the interpolated data is that they cover a large area, have no gap and are freely downloaded. Furthermore, with regard to their use in previous studies, the six gridded data are very relevant for the description of climate variability in the region.
The second type of data, the observations come from the regional network of the synoptic stations (with 34 stations). Despite the gaps in most of the time series, the annual rainfall amount data are available for about 34 stations over more than 40 years. The rainfall data used in this study come from four different sources with two different types: three gridded data (CRU, IRD and UDEL) and the punctual observations collected by the national meteorological services in the countries concerned. The gridded data are scattered at a spatial resolution of 0. 5 • longitude × 0. 5 • latitude. Three generations of CRU data  (1901-1995, 1901-2002 and 1901-2012)  are considered in order to assess their improvement over time. The CRU data  [17]  are produced by the Climate Research Unit and the IRD data are constituted only with rainfall data collected by the IRD  [5, 12].
These two gridded data have been already used in many studies on climate variability and hydrological modeling in west Africa  [18] [19] [20]. The third gridded data of UDEL (University of DELaware) for the 1901-2017 period  [2]  were developed by the University of Delaware (NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www. esrl. noaa. gov/psd/, accessed on 6 May 2021). The gridded data are thereafter designed as from the last year: CRU_1995, CRU_2002, CRU_2009, CRU_2012, IRD_1998, and UDEL_2017. It can be emphasized here that the importance of the interpolated data is that they cover a large area, have no gap and are freely downloaded. Furthermore, with regard to their use in previous studies, the six gridded data are very relevant for the description of climate variability in the region.
Criticism of the Gridded Datasets The quality of the six different gridded data (4 grids from CRU, 1 grid from IRD, and 1 grid from UDEL) is assessed from the comparison with the observations at the monthly and the annual time steps. Some studies  [12, 14, 19, 20]  have shown that even if the gridded data or interpolated data are generated from the observations, they can present some deviations with regard to the raw data. However, only the representativeness of the data is analyzed in this study. Thus, the interpolation methods and the quality of the raw data used by the different institutions are not addressed in this study. The gridded data are compared with observed data of the station located in the mesh and close to the center of the mesh. As there is only one synoptic station per mesh, 34 grid points were selected for the comparison.
The IRD_1998 presents the lowest boxes. The three data (CRU_1995, IRD_1998 and UDEL_2017 present here also the lowest mean absolute errors (<23%) and the highest errors are presented by the CRU_2002. points (Table  1 ) show that the gridded data are significantly different at 11, 12, 12, 11, 5, and 7 stations respectively for CRU_1995, CRU_2002, CRU_2009, CRU_2012, IRD_1998 and UDEL_2017. This test shows again that the IRD-1998 and UDEL-2017data are close to most of the observations. All the CRU grids (CRU_1995, CRU_2002, CRU_2009, CRU_2012), present significant deviations at eleven stations and all the six grids present significant deviations at three stations (Conakry, Natitingou and Diffa). However, CRU_1995 is better than CRU_2002, CRU_2009, and CRU_2012 with regard to the correlation coefficient and the magnitude of the deviation.
5∆Q. The mean absolute error is computed in proportion with regard to the mean of the observed time series. Furthermore, the p-values obtained from the Wilcoxon test for the 34 selected grid points (Table  1 ) show that the gridded data are significantly different at 11, 12, 12, 11, 5, and 7 stations respectively for CRU_1995, CRU_2002, CRU_2009, CRU_2012, IRD_1998 and UDEL_2017. This test shows again that the IRD-1998 and UDEL-2017data are close to most of the observations. All the CRU grids (CRU_1995, CRU_2002, CRU_2009, CRU_2012), present significant deviations at eleven stations and all the six grids present significant deviations at three stations (Conakry, Natitingou and Diffa). However, CRU_1995 is better than CRU_2002, CRU_2009, and CRU_2012 with regard to the correlation coefficient and the magnitude of the deviation.
Assessment of the Breaks Significance The relevance of a break is assessed through some random permutations of the times series. Thus, we have considered three stations (Dori, Tahoua and Sokoto) that present at least two breaks in the time series and four grid points (CRU_1995, CRU_2012, IRD_1998 and UDEL_2017). For each time series, 100 new times series are generated randomly from the random permutations of the raw data. Then the segmentation procedure is applied to each of the new time series. For the three stations, less than 11% (Table  2  for Dori) of the permutated samples have presented a break and non-sample has recorded two breaks together. About 89% of the randomly permutated time series have no break. Thus, the breaks in the raw data are not due to the single value recorded at the break point (year of break) but to a significant change over several consecutive years.
Determination of Wet and Dry Year with regard to the Stationary Period Many studies determine wet and dry year or period from the deviation between the given annual rainfall and a reference threshold  [11, 12]. In this case, the wet year is defined as all year with an amount above the threshold while the dry year is the year with an annual rainfall amount less than the same threshold. This definition of wet and dry year from a single threshold is very limited with regard to the high inter-annual variability of the annual rainfall amount in the region. Instead of the single threshold, we propose in this study to define a reference interval (determined around the average on the reference period) that delineates the area of none significant change with regard to the normal amount. The reference intervals are first assessed from the observed data over the period from 1907 to 1990 at four stations (Ouagadougou, Zinder, Kano and Sokoto).
Determination of Wet and Dry Year with Regard to the Stationary Period Many studies determine wet and dry year or period from the deviation between the given annual rainfall and a reference threshold  [11, 12]. In this case, the wet year is defined as all year with an amount above the threshold while the dry year is the year with an annual rainfall amount less than the same threshold. This definition of wet and dry year from a single threshold is very limited with regard to the high inter-annual variability of the annual rainfall amount in the region. Instead of the single threshold, we propose in this study to define a reference interval (determined around the average on the reference period) that delineates the area of none significant change with regard to the normal amount. The reference intervals are first assessed from the observed data over the period from 1907 to 1990 at four stations (Ouagadougou, Zinder, Kano and Sokoto).
Thus, the significant similarity of the gridded data in the inter-annual variability and magnitude allows these data to be used in the description of the inter-annual variability of the rainfall regime in the region from 1901 to 2018. In the same way, Akinsanola et al. [33]  found also from analysis of five gridded precipitation datasets including CRU_2009 and a previous version of UDEL, that even if these data present some limitations, they could be used for precipitation assessment over West Africa. An assessment of the stationarity in the annual rainfall amount time series shows that the 1901-2018 period presents some discontinuities. All the gridded data and the observed data are not stationary during this period. The discontinuities highlight the high interannual variability of the rainfall regime in the region  [4, 11, 12].
Thus, the significant similarity of the gridded data in the interannual variability and magnitude allows these data to be used in the description of the inter-annual variability of the rainfall regime in the region from 1901 to 2018. In the same way, Akinsanola et al. [33]  found also from analysis of five gridded precipitation datasets including CRU_2009 and a previous version of UDEL, that even if these data present some limitations, they could be used for precipitation assessment over West Africa. An assessment of the stationarity in the annual rainfall amount time series shows that the 1901-2018 period presents some discontinuities. All the gridded data and the observed data are not stationary during this period. The discontinuities highlight the high inter-annual variability of the rainfall regime in the region  [4, 11, 12].
Limitations of the Current Studies In this review, we provided an objective analysis regarding vertical transmission of SARS-CoV-2, but none of the articles found presented conclusive results. For example, even the infants who tested positive using RT-PCR  [19] , were tested 36 h after delivery; thus they had the potential to be infected postnatally. Positive RT-PCR tests for the three neonates diagnosed by Zeng L et al. were performed on days 2, 4, 6, and 7 after birth. The recent review that analyzed 84 retrospectives studies to seek proof of vertical transmission cited previously has taken into consideration new parameters for their analyses like diagnostic analyses, placental analyses and the scientific limitations in interpreting data and not just RT-PCR results in babies. Diagnostic testing combined with incubation period and placental pathology indicates a strong likelihood of a vertical transmission of SARS-CoV-2 from mother to baby is possible  [34].
size We aim to recruit a total of 214 children in both groups. The primary outcome is the difference in the selfreported QoL score with the PedsQL instrument between the children with inherited cardiac disorders and the control group. We used the results from our previous QoL cross-sectional studies in patients with CHD to calculate the sample size  [4, 21, [23] [24] [25] [26] [27]. In our experience, as well as in similar studies using patient related outcomes, a difference of less than 5 points is not clinically relevant, and a difference of more than 10 points is ideal, but rarely achieved  [2, 25, 27]. Therefore, we hypothesized to observe a difference in the total QoL score of 7 ± 15 points (over 100). With a 90% power, a bilateral alpha risk of 5%, and potentially 10% of loss to follow-up or missing data on the primary outcome, we need to include 107 patients in the group 1 and 107 patients in the group 2.
Statistical analysis Patients' characteristics will be presented using mean and standard deviation for continuous variables, and frequencies and proportions for qualitative variables. QoL scores will be calculated with the parametric Student's t test when the distribution was Gaussian and with the Mann-Whitney test otherwise. Association between QoL scores and physical activity, clinical and socio-demographic data will be studied using or Spearman's coe cient. The statistical signi cance will be set at 0. 05 and analyses will be performed using Statistical Analysis Systems version 9 (SAS Institute, Cary, NC, USA). Data will be managed by the clinical research unit of Montpellier University Hospital, France, in collaboration with the paediatric and congenital cardiology department.
The graphs show the distribution of the Young modulus (a-c), the relationship between adhesion force and rupture distance (d-f), the distribution of the length of mannoprotein unfolded (g-i), and the frequency of adhesion events with varying adhesion forces (j-l). In panels j-l, n. a. stands for non-adhesion. Rows A, B and C show results for Cyberlindnera jadinii, Blastobotrys adeninivorans and Wickerhamomyces anomalus, respectively. The data were obtained from 3 cells (3072 curves were analyzed with JPK data processing software before fitting Gaussian curves on the distribution). The blue circle highlights the rupture distance at the adhesion force for ConA. cells  42. However, in fish, the presence of the dectin-1 receptor, along with the entire superfamily V of C-type lectin receptors, is still debatable  43.