dataesr/scientific-paragraphs-categorization huggingface dataset:
It is interesting to note that in the present study, an oil palm gene encoding a PIL type transcription factor was identified in the M_up DEG set, i. e. , displaying an opposing expression profile compared with the SPL genes, as would be predicted from the aforementioned study. As regards the other miRNAs displaying differential accumulation in relation to inflorescence sex, although no direct parallels can be drawn with data obtained from model plants, a functional significance can nevertheless be inferred in the case of miR5179, which putatively targets expression of the oil palm DEFICIENS ortholog EgDEF1, which displayed an inverse profile in our study. The EgDEF1 gene encodes a MADS-box protein that regulates the identity of the corolla and androecium  [35, 36]. Although the inflorescence developmental stages represented in our sampling preceded the differentiation of the reproductive floral organs (i.
For our study, we included in our samples developmental stages from the appearance of floral bracts (male inflorescence) or floral triad bracts (female inflorescence) up to the initiation of floral meristems. Any gene expression or hormonal differences that distinguish the male and female inflorescences within this developmental window, especially in relation to branching, are therefore likely to reflect the difference between the determinate development of the male flowers and the initiation of new branching axes in the flower clusters of the female inflorescence. In this respect, our data fit the general pattern observed with rice, maize, and other species: higher branching activity is associated with increased expression levels of SPL genes, lower accumulation of their miRNA regulators, and higher cytokinin accumulation (Figure  5 ).
Other hormones such as strigolactones might also play underlying roles. Although no biochemical evidence was obtained for the involvement of the latter group in this study, their role in the determination of panicle architecture branching in rice via activation of the OsCKX9 gene has been demonstrated  [50] , and in our dataset, the expression of an oil palm DWARF14-like gene encoding a probable strigolactone esterase  [55]  was observed to be considerably higher (approximately 8-fold) in the male inflorescence. In conclusion, the early sexual differentiation of the oil palm inflorescence to produce male and female forms with different branching architectures is characterized by the differential expression of a number of key genes already known for their morphogenetic importance in other species, some of which are subject to miRNA-dependent post-translational regulation.
nih. gov/assembly/GCF_000442705. 1/ accessed on 1 August 2021) using the MirDeep2 Mapper available on the University of Freiburg Galaxy Server (https://rna. usegalaxy. eu/ accessed on 1 August 2021)  [60, 61]. Read count and mapping statistics for small RNA-seq data are shown in Table  S6. Individual small RNA sequences and their associated counts were extracted from the resulting collapsed reads file by text manipulation. For the purpose of miRNA annotation, a concatenated FASTQ file containing merged cleaned reads from all 6 samples was mapped. From the resulting collapsed reads file, annotation of oil palm MIRNA genes represented in the present dataset was performed by the sRNAanno platform at South China Agricultural University  [21]. In order to study the accumulation of individual miRNAs across the 6 samples, their corresponding counts were normalized to give per million values (Table  S2 ).
Extraction of Total RNAs and Analysis of Differential Gene Expression Total RNAs were extracted from ground frozen inflorescence samples using a Plant RNA Mini Kit (Qiagen SAS, Courtaboeuf, France) following the manufacturer's instructions. Synthesis of cDNA libraries and sequencing (using an Illumina NovaSeq 6000 sequencer) was carried out by Novogene (Cambridge, UK) for samples A05F, A07M, B04M and B05F. In the case of samples A04M and A06F, library preparation and sequencing (using an Illumina HiSeq 2000 sequencer) was carried out by Eurofins (Les Ulis, France). Reads obtained by Illumina sequencing were cleaned as before, then single-end mapped to the reference oil palm genome, using BWA-MEM on the University of Freiburg Galaxy Server  [60]. Read counts were obtained from the resulting BAM files by means of the program Samtools-idxstats.
Read count and mapping statistics for small RNA-seq data are shown in Table  S7. The R-based software package DESeq2  [62]  was used to study differential gene expression. A preliminary heatmap to visualize expression across all samples was obtained using the iDEP server  [63]  using the 50 most variable genes (Euclidean distance; average linkage; Z score cutoff 4; raw count input; centering of genes by mean subtraction; division by standard deviation to normalize samples). Subsequently, the DESeq package was used to identify genes displaying significantly different expression levels in the male and female inflorescences, applying the Padjusted cutoff < 0. 05.
which is in part funded by Collaborative Research Centre 992 Medical Epigenetics (DFG grant SFB 992/1 2012) and the German Federal Ministry of Education and Research (BMBF grants 031 A538A/A538C RBC, 031L0101B/031L0101C de. NBI-epi, 031L0106 de. STAIR (de. NBI)). MGX acknowledges financial support from the France Génomique National infrastructure, funded as part of "Investissement d'Avenir" program managed by Agence Nationale pour la Recherche (contract ANR-10-INBS-09).
Acknowledgments: The authors wish to express their gratitude to colleagues at sRNAanno platform at  South China Agricultural University (Xia lab)  who carried out oil palm MIRNA gene annotation. We also thank  Siwaret Arikit ,  Blake Meyers  and  François Sabot  for helpful discussions at the outset of this work. We thank  NERC Canada  for hormone analyses. The authors acknowledge the  IRD iTrop High Performance Computing (HPC) South Green Platform at IRD Montpellier  for providing HPC resources that have contributed to the research results reported within this paper. (https://bioinfo. ird. fr/;http://www. southgreen. fraccessed on 1 December 2021). We are also grateful for access to the  University of Freiburg Galaxy server , which was used for some calculations and.
Here, we test the existence of periodicities of volcanic degassing at 32 volcanoes using the time series of sulfur dioxide (SO 2 ) emission rates from data of the Network of Volcanic and Atmospheric Change (NOVAC). We use the Lomb-Scargle periodogram to analyze the SO 2 data which allows efficient computation of a Fourier-like power spectrum from unevenly sampled data. We were able to calculate False-Alarm Probabilities in 28 of the 32 volcanoes, and we identified significant periodicities in the SO 2 emission rates in 17 of the 28 volcanoes. However, we find that most of these periodicities are also present in the plume speeds used to determine SO 2 emission rates. Periodicities at about 30-70, ∼120, and ∼180 days were identified at volcanoes located between 16°N and 16°S and are related to intraseasonality and interseasonality in global trade winds and not volcanic in origin.
Plain Language Summary Volcanoes may show cycling patterns in their eruption patterns but also in the geophysical and geochemical data used to monitor them. When these patterns occur at fixed-time intervals, volcanic behavior may be described as periodic. Periodic patterns in volcanic phenomena may reveal the existence of a fundamental process which regulates the inner workings of the volcano and ultimately aid forecasts of eruptive activity. We analyzed the sulfur dioxide degassing patterns of 32 volcanoes from a global database and we reveal significant periodic patterns in 17 of them. However, we also find similar periodic patterns the wind speed of the volcanic plume. We propose that periodic patterns in the degassing rates do not represent physical-chemical processes inside the volcano but result from atmospheric dynamics.
, 2002) , emission rates of SO 2 are now continuously measured at an increasing number of volcanoes. The Network of Volcanic and Atmospheric Change (NOVAC) is the largest instrument network for monitoring volcanic gases globally and comprises a community of volcano observatories and research institutions that apply DOAS to spectra recorded by grounded-based scanning UV-spectrometers  (Galle et al. , 2010). Data obtained by the NOVAC network have contributed to a large database providing time series of SO 2 emission rates from a total of 32 volcanoes  (Arellano et al. , 2021). Owing to a strict data selection protocol and standardized methodology, the database provides time series of good quality, daily SO 2 emissions for a range of volcanoes which cover time scales of just a few days to several years, between 2005 and 2017.
Time Series of SO 2 Emission Rate NOVAC is the largest instrument network for monitoring volcanic gases, with a total of 100 instruments installed across 42 volcanoes  (NOVAC Project, 2022)  and except for a handful of independently run spectrometer networks, data provided by the NOVAC network offers the only continuous daytime recording of volcanic SO 2 emissions  (Galle et al. , 2010). The NOVAC Database (NOVAC Project, 2020) contains time series of SO 2 emissions for 32 of these volcanoes, between 2005 and 2017, but the completeness of the data is variable (Figure  1 ). Emissions of SO 2 are derived from radiance spectra which are recorded by a series of instruments which scan the horizon at steps of 3. 6°. Each of these spectra are analyzed using DOAS technique to retrieve a Slant Column Density (SCD) of SO 2 from spectra with elevation angles ranging from ±90° from zenith.
, 2021). Emission rates of SO 2 incorporated in the NOVAC Database are calculated using wind speed from a reanalysis data set  (Dee et al. , 2011)  provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent plume speed  (Arellano et al. , 2021). Table  1  gives a brief description of the parameters, in addition to the measured SCD, which contribute to generating the reported daily SO 2 emission rate. Some parameters are obtained from the (modeled) meteorological data set (e. g. , cloud cover), some are measured (e. g. , plume width) and some refer to the validity of measurements (e. g. , number of total and valid measurements in a day). Each parameter may therefore have a different contribution to the final Wind velocity obtained from the ERA-Interim database  (Dee et al.
, 2011)  of ECMWF is used, with a grid resolution of 0. 125 × 0. 125°, 6 hr and up to 60 vertical levels from ground up to 0. 1 hPa. Data are interpolated to the location of the volcanic vent and time of measurement for each emission rate calculation. Atmospheric pressure is converted to vertical height using the L60 model definitions of ECMWF (U. S. Standard Atmosphere, 1976).
Cloud cover Daily mean of modeled cloud cover at summit altitude % Cloud cover is taken from the ERA-Interim database  (Dee et al. , 2011)  of ECMWF, retrieved every 6 hr on a horizontal grid of 0. 125 × 0. 125° (13. 9 × 13. 9 km for mean Earth radius) surrounding the location of the main volcanic vent. It is then further interpolated to the vent location and the time of each scan.
Error in the Time Series of SO 2 Emission Rate Error in the reported SO 2 emission rate arises from uncertainty in the measurement of SO 2 (e. g. , spectroscopic error), as well as the variables used to derive emission rates (e. g. , wind speed). The SO 2 emission rates used in this study, despite noncontinuous, are considered to have good quality. Compared to typical NOVAC measurements where total error may exceed 100% in poor conditions  (Galle et al. , 2010) , SO 2 emissions data in the NOVAC Database, which are used in this study, have a minimum uncertainty estimate between -30% and 10%. One of the biggest challenges in deriving accurate SO 2 emission rates is obtaining accurate wind speeds at the altitude of the volcanic plume  (Arellano et al. , 2021). In addition to plume velocity, the main source of uncertainty is atmospheric and in-plume scattering which contribute to error in the spectroscopic retrieval of SO 2  (Kern et al.
As is the case for frequency analysis techniques, the Lomb-Scargle method can be used to detect periodicities at frequencies between twice the sampling rate and half the data set length  (Nyquist, 1928). We considered the daily mean SO 2 emission rate and also incorporate the standard deviation to account for the large variation in SO 2 emission rates recorded throughout the day. We applied a bootstrapping approach to calculate the PSD from resampled time series of the daily SO 2 emission rates 1,000 times, each time selecting a random value (mean ± standard deviation). By doing so we assume the distribution of values is uniform for each day which is not always the case. While the first, second, and third quartiles of daily SO 2 emission rates are included in the NOVAC Database, we have taken this standard approach to enable the comparison of PSD between parameters (Table  1 ) where percentile information is not provided.
We used MATLAB's Plomb function  (MathWorks, 2021)  to calculate the Lomb-Scargle periodogram of each bootstrapped time series. Since the SO 2 emission rate is reported as a daily mean, we use a sampling rate (ΔT) of 1 day to calculate the PSD estimate. We use an oversampling factor (ofac) of four, which resembles the zero-padding technique for FFT-based methods and is used to improve the resolution of the frequency grid over which the PSD is calculated. The maximum frequency is determined by 1/(2 × ΔT), which for uniformly spaced signals corresponds to the Nyquist frequency  (Nyquist, 1928). We refer to this here as the pseudo-Nyquist frequency, which represents the shortest period we are capable of detecting based on the ΔT (2 days). The minimum frequency is defined by 1/(ofac × N × ΔT), where N is the number of days between the start and end dates of the time series and not necessarily the number of datapoints.
Mayon Volcano The time series of daily mean SO 2 emissions at Mayon volcano, Philippines  (Bornas et al. , 2020)  vary between about <1 and 17 kg s -1 , as described by 102 data points between August 2011 and January 2017, corresponding to about 5% of days with data (Figure  3 ). The variation of SO 2 emission rates obtained from measurements within any single day may exceed 17 kg s -1. The Lomb-Scargle PSD median estimates for the time series of SO 2 emission rate between August 2011 and January 2017 are below the 50% FAP threshold, except for one peak at 51 days and another at 99 days which surpass the 1% and 50% FAP thresholds, respectively (Figure  4 ). We also performed Lomb-Scargle analysis on the range of parameters involved in the calculation of the SO 2 emission rate and found that only the plume speed obtained from the ECMWF ERA-Interim database (see Table  1 ) has corresponding 51-day and 99-day periodicities with FAPs of 1% (51 days) and 50% (99 days) (Figure  5 ).
Other Volcanoes in the NOVAC Database We applied the same methodology to 28 volcanoes of the NOVAC network and we found multiple median PSD estimates which reach the 50% FAP threshold (Figure  C1 ). Several periodicities have FAPs of 0. 01%, enabling the rejection of the null hypothesis (that no periodic trend exists) with at least 99. 99% confidence. We found that many volcanoes show a similar trend to the Mayon data, whereby periodicities identified in the time series of SO 2 emission rates are also present in the plume speed used to derive these emission rates (Figure  8 ). We attempted to summarize these relationships by categorizing volcanoes in four different fields according to the periodicities found in SO 2 emission rate and plume speed (Figures  6  and 9 ). Periodicities were not identified (FAP > 50%) in either parameter at six of the volcanoes (21% of the total): Arenal, Copahue, Fuego, Piton de la Fournaise, Santa Ana, and Vulcano.
Systematic Error in the Plume Speed Used to Derive SO 2 Emission Rates For measurements conducted under ideal conditions, it is well-known that plume speeds are the main source of error in SO 2 emission rates  (Arellano et al. , 2021)  and correlations have been observed between SO 2 emission rate and wind speed at several volcanoes (e. g. ,  Dinger et al. , 2021). The fact that these correlations translate into periodicities implies a systematic error in the calculation of SO 2 emission rates. A simple rationale would be the limitation in using wind speeds derived from the ECMWF ERA-Interim database to accurately represent plume velocity. Oversimplified representation of complex topography has been proposed to explain the differences between ECMWF ERA-Interim data and observations of surface conditions (e.
One improvement may be to use the ICA of SO 2 together with the time series of plume speed. If the trend in-plume speed changes without an accompanying adjustment in the measured ICA, the disparity may reflect a change in degassing rate rather than atmospheric dynamics. The contribution of this study is to demonstrate that the dependencies between wind speed and SO 2 emissions rate translate into periodicities, but further work is needed to understand the magnitude of this effect. Given that the NOVAC Database contains substantial data on SO 2 emissions which have been determined with standardized methodology, this data may be used to identify the range of SO 2 emissions which are derived under and given atmospheric conditions. It may be advantageous to provide a quantitative statement which refers to the parameters used to derive the reported SO 2 emission rate.
Previous Works on Periodicities of Volcanic Degassing Previous studies of SO 2 emission rate at various volcanoes have identified several periodicities. For example,  Pering et al. (2019)  found multiple periodicities of 46, 94, 121, and 140 days in the emission rate of SO 2 at Masaya volcano determined using the same modeled wind speeds taken from the ECMWF ERA-Interim database  (Dee et al. , 2011)  and suggested that these could be linked to cycles of magma replenishment. Our analysis of SO 2 emission rates between April 2007 and October 2014 shows a cluster of statistically significant periodicities between 83 and 185 days, the most significant at 137 days (FAP < 0. 01%; Figure  C1 ). Notwithstanding we also found that most periodicities in SO 2 emission rate are associated with periodicities in the plume speed (Figure  8  and Appendix D).
Conclusions and Future Work We have identified significant periodicities in the SO 2 emission rates in 17 out of the 28 volcanoes included in the NOVAC Database. About 50% of the volcanoes analyzed show significant correlation between periodicities in SO 2 emission rate and the plume speed used to derive emission rates. Ten volcanoes (36% of the total) appear to show periodicities in SO 2 emission rate that are unaccompanied by periodicities in-plume speed. However, detailed inspection of all parameters involved in determining the SO 2 emission rates show that only those from Fuego de Colima volcano may be of magmatic origin. For all other volcanoes, periodicities in SO 2 emission rate are associated with periodicities in-plume speed or other parameters. Periodicities occur at about 30-70, ∼120, and ∼180 days in SO 2 emission rate and these appear to be related to the intraseasonality and interseasonality in global trade winds.
Specifically, those between 30 and 70 days likely relate to variation in wind speed according to the Madden-Julian Oscillation. Our findings show SO 2 emission rates and the wind speed used to derive them have common periodicities. We do not find a common causation between wind speed and SO 2 degassing and thus we suggest that there is a systematic bias in the calculation of SO 2 emission rates. Inaccuracies may arise due to the limitation of using wind speeds derived from the ECMWF ERA-Interim database but may also occur when the wind speed data are of limited temporal or spatial resolution to accurately represent plume velocity at the measurement time and location. This highlights the critical importance of having accurate wind data for deriving realistic SO 2 emissions, particularly when identifying periodic trends related to volcanic behavior.
Horizontal lines correspond to False Alarm Probability (FAP) of 50%, 10%, 1%, and 0. 01% (where some labels have been omitted for readability), see Table  C1  for PSD threshold values associated with FAP at each volcano. and for valuable discussions during the initial stages of this work as well as to M. Nguyen for their expert advice on time series analysis. We are especially grateful to all NOVAC members who have contributed to establishing the NOVAC Database which ultimately made this study possible. We are grateful to M. Poland for their suggestions and thank T. Lopez and one anonymous reviewer for their constructive reviews which helped improve the quality of this manuscript. This research was supported by the Earth Observatory of Singapore via its funding from the National Research Foundation Singapore and the Singapore Ministry of Education under the Research Centres of Excellence initiative.
Data Availability Statement All SO 2 emissions data and associated parameters used in this study are available via the NOVAC Database (licence CC BY 4. 0) which may be found at https://novac. chalmers. se/ (NOVAC Project, 2020). Data from the NOVAC Database may be used providing all organizations and individuals are properly credited (https://novac. chalmers. se/datauseagreement). Zonal wind speed data used in this study are available through the European Centre for Medium-Range Weather Forecasts (ECMWF) at http://www. ecmwf. int/. © [2022] European Centre for Medium-Range Weather Forecasts  (ECMWF, 2022). This data is published under a Creative Commons Attribution 4. 0 International (CC BY 4. 0) https://creativecommons. org/licenses/by/4. 0/ and ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.
We use MATLAB R2021a for all analysis which makes use of the "Plomb" function available via the Signal Processing Toolbox™. A copy of the code used in this manuscript is available at: https://zenodo. org/ account/settings/github/repository/cbarrington22/Periodicities together with a user-friendly code which enables implementation of the method presented in this manuscript (https://doi. org/10. 5281/zenodo. 7304985).
The model revealed the same CoP related to another protein-based vaccine but not with mRNA-1273 vaccine We took the opportunity of another study testing a two-component spike nanoparticle vaccine performed in the same laboratory and using the same immune and virological assays  (37)  for applying the proposed model and methodology. In this study, 6 animals were vaccinated and compared to 4 naive animals (Figure  S8A, S8B ). The good fit of the data (Figure  S8C, S8D ) allows for estimating the effect of the vaccine that appeared here also to decrease the transmission rate (by 99%) and increase the clearance of the infected cells by 79%. Looking at the best mechanistic CoP following the previously described strategy, we ended here again with the inhibition of RBD binding to ACE2 as measured by ECL RBD.
Anti-RBD and anti-N IgG were titrated by ELISA. The Nucleocapsid and the Spike RBD domain (Genbank # NC_045512. 2) were cloned and produced in E. Coli and CHO cells, respectively, as previously described  (31). Antigens were purified on C-tag column (Thermo Fisher) and qualitycontrolled by SDS-PAGE and for their level of endotoxin. Antigens were coated in a 96 wells plates Nunc-immuno a isorp (Ther o isher) at 1 μg/ in carbonate buffer at 4°C overnight. Plates were washed in TBS tween 0. 05% (Thermo Fisher) and blocked with PBS 3% BSA for 2 hours at room temperature. Samples were then added, in duplicate, in serial dilution for 1 hour at RT. Non-infected NHP sera were used as negative controls. After washing, anti-NHP IgG coupled with HRP (Thermo Fisher) was added at 1:20,000 for 45 min at RT.