dataesr/scientific-paragraphs-categorization huggingface dataset:
05. Principal component analyses (PCA) were also carried out within the DESeq2 package  76. Counts were transformed using the varianceStabilizingTransformation function, and the PCA was calculated and plotted using the plotPCA function. This function carries out a PCA on the top 500 genes, based on variance. A weighted gene co-expression network (WGCN) was generated with these gene expression counts, using the R package WGCNA  37. Normalized counts were extracted from the DESeq2 dataset with the counts function. These data were filtered for genes with zero variance or with missing values with the WGCNA function good-SamplesGenes. With the remaining 9631 genes, a signed WGCN was created using a soft power of 14, implementing the biweight midcorrelation calculation and setting the minimum module size to 30.
Modules with a dissimilarity less than 0. 5 were merged using the mergeCloseModules function. We related the expression profiles of the resulting nine modules to castes and queen stages by correlating (Pearson's r) the module eigengenes (first principal component of the expression matrix of each module) with a binary vector, containing 0 s and 1 s depending on the membership of each sample (FW, QT0, QT1, QT2, QT3, QT4, or KT4). A significant positive correlation signifies an overall upregulation while a negative correlation signifies a downregulation of expression within the module for a given caste or queen stage. To visualize the WGCN, we first reduced the WGCN to include only the most highly connected nodes. We did this by retaining genes with a topological overlap of at least 0. 2 with at least another gene, and by including the top 15 most connected genes within each module.
This reduced WGCN (5823 genes) was exported to Cytoscape (version 3. 8. 0  77 ) with the exportNetworkToCytoscape function in WGCNA (threshold 0. 15). In Cytoscape, the network was rendered using the Edge-weighted Spring Embedded Layout and nodes were colored by module membership or expression fold change. GO-term enrichment analyses were carried out with topGO (version 2. 34. 0 78 ), using the classic algorithm. Node size was set to 5, Fisher exact tests were applied, and we only kept GO terms that matched with two genes at least and with a P value <0. 05. We established orthology of M. natalensis genes to D. melanogaster (v. 6. 12) and H. sapiens (hg38) using the method of reciprocal best BLAST hit  79. For this, the proteomes were blasted against each other using BLASTp (BLAST 2. 7. 1 + 80 ) and an e-value threshold of 1e-5.
Reciprocal best BLAST hits were extracted from the output files using a custom python script. Analysis of ILPs. Two ILP genes (Mnat_00258 and Mnat_03820) were found in the M. natalensis proteome based on sequence similarity to ILPs in D. melanogaster, using BLASTp (BLAST 2. 7. 1 + 80 ) with a e-value threshold of 1e-5. We checked for further ILP genes within the genome by mapping the protein sequences of these two M. natalensis genes and eight known D. melanogaster ILPs (downloaded from NCBI; accessed February 2021) against the M. natalensis genome. This was carried out with EXONERATE (v 2. 2. 0 81 ) using the protein2genome model at default settings. No further ILP copies were found but the annotations of the two M. natalensis genes were improved based on these exonerate alignments. We searched for ILP orthologs within 25 further insect proteomes (see table in Fig.
4  for full details) using BLASTp and an e-value threshold of 1e-5. The protein sequences were aligned with t-coffee 82 in accurate mode, which incorporates both sequence profile and structural information. We trimmed the alignment with tri-mAL in the automated1 mode  83  , then created a gene tree with iqtree2  84. This program automatically selects the best-fit model with ModelFinder and carries out bootstraps. With these methods, we recreated a gene tree Q. pfam+R4 model and 10,000 bootstraps. The tree was visualized with the online tool, iTOL v6 85.
The acquisition was made in full-scan mode between 100 m/z and 1700 m/z, with a scan of two spectra per second. Two internal references were used for in-run calibration of the mass spectrometer (121. 0509, 922. 0098 in positive ion mode and 112. 9856, 1033. 9881 in negative ion mode). MassHunter B. 07 software allowed us to control the parameters of the machine acquired. Data processing and annotation. MsDial v 4. 0 software 91 was used for data processing and annotation of lipids. The data collection was performed by fixing the MS1 and MS2 tolerance, at 0. 01 Da and 0. 025 Da, respectively. The peak detection was fixed at 1000 amplitude and a mass slice width at 0. 1 Da. The deconvolution parameters correspond to a sigma window value at 0. 5 and a MS/MS abundance cut off at 10 amplitude. Isotopic ions were kept until 0.
5 Da. The peaks list was annotated based on their unique MS/MS fragmentation patterns using the in-built LipidBlast mass spectral library in MS-DIAL software. The accurate mass tolerance MS1 and MS2 were fixed at 0. 01 Da and 0. 05 Da, respectively. The identification score cut off was fixed at 80%. Lipids were normalized by the intensity of the internal standard (PC 31: 1 | PC17:0-PC14:1). Ploidy analysis. Fat bodies from QT0, QT1, QT2, QT3, and QT4 were processed by Flow Cytometric Analysis with a Cycletest PLUS DNA Reagent Kit (BD Biosciences, Le pont de Claix). Number of replicates per group are provided in Supplementary Table  1. All procedures were adapted from Nozaki & Matsuura  41. Stained nuclei were analyzed for DNA-PI fluorescence using an Accuri C6 Flow Cytometer (BD Biosciences) at an excitation wavelength of 488 nm and a detector equipped with an 585/45 bandpass filter.
Approximately 1000 cells were acquired for each measurement. Flow cytometric analyses were performed with the Accuri C6 software v1. 0. 264. 21 (BD Biosciences). Debris was removed on an FSC-A/SSC-A dotplot and doublet was eliminated with PI-FL2-H/ FL2-A dotplot. The nuclei were analyzed with a histogram PI-A. The 1C DNA peak was determined by the analysis of king's testis (sperm), allowing the identification of the 2C, 4C, and 8C peaks of the others samples.
To compare log-transformed individual lipid values between FW and QT4, or between QT0, QT2, and QT4, Welch tests were used, corrected for multiple testing using the Benjamini-Hochberg method. To compare the peroxidation index (PI) in fat bodies of different queen stages we used Kruskal-Wallis test followed by Dunn's post hoc comparisons. All tests were considered significant for a P value <0. 05 and were carried out using R software (v 4. 0. 2). Heatmaps were made using the heatmap function in the Metaboanalyst v 4. 0. with Euclidean distance and clustering using Ward's method. Ploidy analyses. To compare percentages of nuclei with different multiples of haploid genomes between queen stages, we used permutational MANOVA followed by pairwise post hoc perMANOVA (Holm-Bonferroni correction for multiple comparisons).
Acknowledgements The authors would like to thank  Dr. Christian Bordereau  and  Dr. Jannette Mitchell  for helpful discussions on the termite model. The authors acknowledge the  Department of Public Works and Infrastructure, Pretoria , and the  University of Pretoria  for allowing access to their sites of collection of M. natalensis. The authors also thank  Cyril Fresillon  and  Pierre Deparscau , photographers for the CNRS images. We thank  Qi Li  for termite colony maintenance. This study was supported by the  International Human Frontier Science Program   RGP0060/2018  to M. V-C. S. S. was also supported by a fellowship from  Université de Paris Est-Créteil (UPEC). A. L. acknowledges financial support from  France Génomique  ( ANR-10-INBS-09-08 ). We would like to thank the  EcoChim platform of the University of Rennes  for access to metabolomics facilities.
Data availability RNA-seq reads generated in this study have been deposited in Sequence Read Archive (BioProject ID: PRJNA685589 and BioSample accessions: SAMN17088123-SAMN17088147). The normalized expression data, differential expression results, ILP annotations, and ILP sequences are available as Supplementary Data 3-6. Further data and scripts that support the findings of this study are available in Dryad with the identifier https://doi. org/10. 5061/dryad. 51c59zw7t. All other data are available from the corresponding author on reasonable request.
LC-HRMS Data Processing Raw LC-HRMS data were converted to m/z extensible markup language (. mzXML) in centroid mode using MSConvert ProteoWizard (release version 3. 0. 20350-23fa10407). Preprocessing of the data was performed on MzMine 2. 53  [21]. Normalization and statistical analyses were performed on MetaboAnalyst 5. 0  [22]. Mass detection was realized keeping the noise level at 5. 0E6 for MS level 1 and 1. 0E4 for MS level 2. ADAP chromatogram builder was employed using a minimum group size of scans of 4, a group intensity threshold of 3. 0 × 10 6 , and an m/z tolerance of 0. 001. ADAP wavelet deconvolution algorithm was used with the following standard settings: S/N threshold of 10, coefficient/area threshold of 20, peak duration range of 0. 00-4 min, and RT wavelet range of 0. 01-0.
Based on accumulated knowledge of PAs metabolic pathways in E. coli, we hypothesized that this accumulation was related to a normal response of the bacterium to limit PA toxicity. We have abolished this effect by increasing the expression of the two-component exporter system MdtI/MdtJ. This strategy significantly increased the amount of spermidine present in the extracellular space, while limiting the acetylation of spermidine by EcN. We have also showed that spermidine production was influenced by the availability of putrescine in the environment of the bacteria. Finally, we evaluated the impact of our engineering on the metabolomes of the strains. The data generated showed that the metabolic engineering we performed has a limited impact on the overall metabolome of the strains and that this impact seems to target the metabolites related to the PA pathway.
Funding: A. L. G, and B. T were supported by  "Vaincre la mucoviscidose" (VLM)  and  "Association Grégory Lemarchal" (AGL)  (Grant number  RF20180502189  and  RF20190502416 ) and  ANR-15-IDEX-02 , and  Fondation Université Grenoble Alpes. Part of this work has been performed at the  Gemeli-GExiM metabolomics platform. Institutional Review Board Statement "Not applicable" Preprints  (www. preprints. org)| NOT PEER-REVIEWED | Posted: 17 October 2022 doi: 10. 20944/preprints202210. 0239. v1.
With the advent of the second and third IASI instrument, together with the improvement of retrieval algorithms over time, a number of short-and long-lived trace gases were identified in the IASI spectra up-or downwind of strong emission sources  (Clarisse et al. , 2011; De Longueville et al. , 2021). This paper describes the potential of the PCA applied on the IASI L1C (apodized radiance) data for the automatic, near-real-time detection and characterization of exceptional events. The paper is organized as follows: Sect. 2 describes the IASI instrument and the dataset used in this study. Sec-tion 3 describes the PCA method. In Sect. 4, an innovative approach based on the PCA method and IASI data granules is presented, which allows the spectral characterization of species in near-real time. In Sect.
5, different case studies of exceptional past events are discussed, such as volcanic, fire, and anthropogenic pollution episodes, along with industrial accidents, detected by IASI/Metop-A and Metop-B. Finally, conclusions are given in Sect. 6. 2 The IASI radiance data IASI is a Fourier transform infrared spectrometer, which records the thermal infrared (TIR) radiation emitted by the Earth and the atmosphere, between 645 and 2760 cm -1 , with 8461 channels sampled every 0. 25 cm -1 and a spectral resolution of 0. 5 cm -1. An example of IASI spectrum along with the absorption band of several species is illustrated in Fig. 1. In this work, IASI-A and IASI-B are used as a combined dataset. The IASI-A dataset is used for the study of events before the launch of IASI-B and for creating the PCA training database (described hereafter), and the IASI-B complete dataset is used for data after 2013 to the present.
The two datasets have been shown to be highly consistent, with no significant drifts over time  (García et al. , 2016). Each IASI instrument provides more than 1. 2 × 10 6 spectra per day. IASI L1C data are disseminated by EUMETSAT in 3 min files (hereafter called a granule) less than 3 h after each overpass. Each granule contains 22 or 23 IASI scan lines, with 120 pixels per line. With a wide swath width of ∼ 2200 km, global observations are provided twice a day, at 09:30 and 21:30 LT. IASI has an instantaneous field of view (FOV) at nadir, with a spatial resolution of 50 km × 50 km, composed of 2 × 2 circular pixels (IFOV), each corresponding to a 12 km diameter footprint on the ground at nadir  (Clerbaux et al. , 2009). The atmospheric concentrations of some species are routinely retrieved from the spectral signatures  (George et al.
, 2009; Clarisse et al. , 2011; Van Damme et al. , 2014)  and distributed through the AERIS database (http://iasi. aeris-data. fr, last access: 19 April 2023). Some exceptional events have been studied in detail, such as the 2010 Russian fires  (R'honi et al. , 2013) , pollution in the North China Plain  (Boynard et al. , 2014) , and SO 2 anthropogenic pollution  (Bauduin et al. , 2014 (Bauduin et al. , , 2016)). 3 The principal component analysis method.
Basic concepts The PCA method for high spectral resolution sounders, such as IASI, is described in  Atkinson et al. (2008). This method is well suited to efficiently represent the amount of information contained in the 8641 IASI channels. It relies on the use of a dataset of thousands of spectra representing the full range of atmospheric conditions from which the principal components are calculated -the so-called training database. One considers an ensemble of n IASI radiance spectra y of dimension m (where m is the number of channels, and n is the number of observations). Let us denote N -1 y as the mean and S (m×m) as the covariance of the normalized ensemble of spectra. N is the noise normalization matrix and is defined as the square root of S y (m×m), which is the instrument noise covariance matrix associated with the IASI spectra.
Physically, the GMI (GMA) pseudo-residual is associated with reconstruction errors in spectral absorption (emission) lines. Since the method is based on the granule extrema (GMI and GMA), the method is therefore called IASI-PCA-GE, where GE stands for granule extrema. It is important to note that these pseudo-residuals associated with a granule are different from the individual IFOV-residual associated with each IFOV. Figure  2  illustrates an example of GMA and GMI pseudoresiduals for an intense fire event that occurred in Australia on 1 January 2020. The GMI pseudo-residual (bottom panel) is characterized by detectable spectral features asso- ciated with a poor reconstruction around 700, 950, 1100, or 2100 cm -1. Using a spectroscopic database allows us to associate some of these strong peaks with the contributions of different atmospheric components (see Sect.
Detection thresholds Two detection thresholds are defined in order to select (1) the granules associated with outliers only (which allows us to gain computation time) and (2) the IFOV-residuals associated with reconstruction errors. For the definition of the detection thresholds, a dataset of 43 000 IASI/Metop-B granules (21 500 granules for daytime and 21 500 for nighttime) containing outlier and regular spectra and chosen randomly on the first of each month between April 2013 and April 2021 is used. Note that this dataset differs from that generated for the principal component calculation, as the detection method is applied on a granule basis. From this dataset, 21 500 GMI and 21 500 GMA pseudo-residuals are calculated for both day-and nighttime conditions. Figure  3  shows the statistical distribution of the largest minimum and maximum values for each of the 43 000 GMI/GMA pseudo-residuals for all channels.
The lower and upper limit of the blue box represents the 25th and the 75th percentiles in the data, respectively. The red line represents the median. The black lines represent upper adjacent value (UAV) and lower adjacent value (LAV), and the red crosses have been considered to be outliers in a first analysis of the dataset. Using the UAV and LAV as thresholds was observed to be too restrictive. After several tests, a decision was made to use the 25th percentile of the data to keep the granules as- sociated with potential outliers (F 1 threshold). All granules associated with GMA or GMI minimum and maximum values (in absolute values) larger than the 25th percentile of the datasets are then selected, thus avoiding the need to process granules without interesting anomalies. A second threshold (F 2 threshold) was defined for each spectral channel, based on the 99th percentile value of the GMI and GMA pseudo-residuals calculated from the 43 000 granules (21 500 for daytime conditions and 21 500 for nighttime conditions).
Volcanic eruption archive for IASI/Metop-B The time series of the SO 2 detections derived from the IASI-PCA-GE method is applied to the IASI/Metop-B global dataset over the 2013-2022 period. Figure  6  shows the comparison of the SO 2 IASI-PCA-GE signal intensity with the SO 2 hyperspectral range indexes (HRIs) product at 5 km  (Bauduin et al. , 2016). HRIs at 5 km are chosen because of a good sensitivity around this altitude  (Clarisse et al. , 2014) , compared to the L2 SO 2 concentration data that show concentrations above 5 km (likely high-intensity volcanism). Only daily SO 2 extrema of both the IASI-PCA-GE method and HRI product are compared. They are spatially co-located and associated with documented volcanic events from the   over 3373 d in total), an excellent correlation between HRIs and IASI-PCA-GE signal intensity (R 2 = 0.
96) is found between the two datasets. In order to analyze and understand the differences between the two records, the correlation between the latitudes of both datasets shown in Fig. 6  are plotted (see Fig. 7 ). An excellent location correlation between both HRI and IASI-PCA-GE methods is observed for high-intensity detections. However, some discrepancies are found in the case of low-intensity events, corresponding to commonly active degassing volcanoes. Some specific latitudes associated with degassing volcanoes, such as Sabancaya  (Moussallam et al. , 2017) , the Vanuatu island arc with Ambae  (Bani et al. , 2012) , Colima and Popocatepetl in Mexico  (Varley and Taran, 2003) , and the The daily maxima located around 56 • N have been investigated and found to be associated with Kamchatka degassing volcanoes.
However, some discrepancies are found in terms of location and inten-sity. A few pixels are detected by the IASI-PCA-GE method in the southeast of Australia, which is in agreement with the CO operational L2 product. However, the retrieval method is able to detect a larger plume over Australia compared to the IASI-PCA-GE method. Furthermore, a large plume is also detected over the Pacific Ocean but is missed by the IASI-PCA-GE method. Note that the high-intensity CO peaks are clearly detected in the residuals (see Fig. 10 ). However, most of the missing pixels in the PCA detection results are located above the sea. That could be due to the combination of the database chosen in the PCA method and the high variability in this spectral domain. Indeed, a higher thermal contrast variability is observed above land  (Clerbaux et al.
, 2009) , but the database contains spectra representing the natural variability without differentiating between sea and land pixels. As a result, the spectral reconstruction above the sea with the PCA method will be less sensitive to spectral variations, causing a reduced sensitivity above the sea. Furthermore, the spectral region between 2050 and 2200 cm -1 has shown a large statistical distribution of extrema signals within the 21 500 granules used for threshold calculation in Sect. 4. 2, allowing us to set a restrictive threshold for the outlier detection for CO. That restriction will also impact the num- ber of detected pixels. The sensitivity of PCA reconstruction outliers to strong CO concentrations in fires should be more deeply investigated in further studies. NH 3 is also retrieved in near-real time (Van Damme et al.
Meas. Tech. , 16, 2107-2127, 2023 1345. 00 cm -1 ; see Fig. 11 ) is found by the IASI-PCA-GE method. Figure  12  (right) illustrates the spatial distribution of NH 3 and CO total column and SO 2 plume altitude L2 data retrieved from the IASI/Metop-A L1C data  (Clarisse et al. , 2012). The retrieval and IASI-PCA-GE methods shows different patterns. We clearly see two plumes for SO 2 plume altitude and CO concentrations, but only a few pixels of detection are found for NH 3.
C 2 H 4 sporadic emission at the border of Iran/Iraq In Sect. 5. 2. 2, we reported that the IASI-PCA-GE method is well suited to the detection of biomass burning by using the C 2 H 4 indicator found in conjunction with other signatures of molecules usually associated with fire activity. Among the events that we detected, on a few occasions, we found intense signatures in the Iran/Iraq region with no absorption other than C 2 H 4 , which suggests that sources other than biomass burning -likely due to anthropogenic activitiesare at play. The main event occurred in July 2021, and some other weaker ones are also identified in Fig. 10. By averaging IASI data over time and using a super-sampling technique,  Franco et al. (2022)  uncovered and identified over 300 worldwide emitters of C 2 H 4 emanating from petrochemical clusters, steel plants, coal-related industries, and megacities.
Conclusions and perspectives This paper presents an innovative approach, based on a PCA method applied on the IASI radiance spectra, that allows the detection and characterization of exceptional events in near-real time. This new method, the IASI-PCA granule extrema (GE) method, consists of focusing on extrema calculated within a given geographical region. A statistical selec-tion is made by focusing on the anomalous variability in IASI channels (detection of outliers) in order to identify the contribution of specific molecules from different types of events. The method is applied to the standard 3 min granules of IASI observations, thus allowing the near-real-time detection of a series of short-lived trace gases. Using a dataset representing the full range of atmospheric conditions, we show that the PCA method is well suited to detecting outliers efficiently.
The method also proves useful for deriving consistent, long-term records of fire and volcanic events, and the data will continue to accumulate over time, as the method is now routinely implemented. Further work is still needed to avoid false detections, such as those associated with HNO 3 , which are due to the correlation between different absorption bands for the same molecule, and one of them is likely interfering with the SO 2 present in the volcanic or industrial plumes. https://doi. org/10. 5194/amt-16-2107-2023 Atmos. Meas. Tech. , 16, 2107-2127, 2023 A first version of this method is currently running continuously, delivering email alerts on a routine basis, using the near-real-time IASI L1C radiance data. Although the method is still being tested, it is planned to be used as an online tool for the early and systematic detection of extreme events.