dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This research was financially supported by the  National Social Science Foundation of China  (No. 17CJY022 ) , the  National Scholarship Foundation  (No. 202008430028 ), the  Think Tank Project of Hunan Provincial Federation of Social Sciences  (No. ZK202011 ) and  Hunan Graduate Science and technology Innovation Project  (No. CX20210908 ) for which the authors express their appreciation.
These growth-related secondary sexual traits have received considerable attention in developmental genetics, but we still lack a general understanding of the genomic regulation underlying their development  [13, [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27]. In addition, studies of sexual dimorphism tend to focus on adult gonads or whole-body transcriptomic datasets, which are unsuited for understanding how secondary sexual characters are built during development and their possible consequences on genome evolution  [4, 5, [28] [29] [30] [31] [32] [33]. Conversely, while some studies in flies examined sex-biased gene expression underlying sex differences in bristle patterns  [34] , most studies across tissues and developmental stages lack comparisons between the sexes  [35, 36]. We, therefore, know little about which sets of developmental genes are associated with trait exaggeration, whether they present a pattern of sequence evolution or whether they tend to be arranged in any specific genomic organization.
Over 90% of M. longipes genome is represented in the thirteen largest scaffolds (Additional file 1: Table  S1 ). We then used automatic genome annotation, supported by de novo transcriptome-based gene models, to build the gene set of M. longipes (see the "Methods" section). This analysis predicted 26,130 genes and 27,553 transcripts. BUSCO analysis, based on the 2018 insect dataset  [45] , revealed that 96% of gene models are present; among these 92% are complete, 3% are fragmented and 1% are duplicated (Additional file 2: Figure  S1 ). We therefore conclude that M. longipes genome is near-complete.
3% of variation in gene expression), and the legs of the same sex in PC3 (10. 2% of the variation in gene expression) (Fig. 1f ). We conclude that the three main components involved in leg length variation were retrieved in our transcriptome datasets. Yet, conversely to leg morphologies, homologous legs from the two sexes are now more divergent in terms of gene expression than legs from the Fig. 1  a Microvelia longipes in the wild. b Sexual dimorphism in the legs, showing differences in length and male-specific sex combs in the firstlegs (Inset). c, d Principal component analysis (PCA) on measurements of male and female leg length from the long-leg and short-leg selected inbred populations, also used for the transcriptomic analyses  [41]. c The first principal component (Dim1) explains primarily differences between legs of the same sex while the second PCA (Dim 2) explains differences between inbred populations, specifically in males.
d The third PCA (Dim 3) explains the differences between the sexes. e, f Principal Component Analysis on the whole transcriptomic dataset. e The three first PCAs (Dim1, 2, 3) recapitulate the variance between the Big (blue) and Small (green) lines. f Within-class analysis after correcting for line effects. Dimension 1 separates sexes while Dimension 3 separates legs. The inset represents the within-class correction for the line effects same sex, consistent with previous findings in flies  [34]. We hereafter focus on the effect of sex on gene expression as it potentially represents a major factor underlying leg exaggeration through differences in allometric coefficients  [41]  (Additional file 3: Figure  S2 ).
Molecular function of sex-biased genes Finally, we aimed to determine the molecular function of the sex-biased genes in our dataset. Gene ontology (GO) term analyses revealed enrichment in translation, metabolic processes, and Wnt signaling pathways for the male-biased genes in the third legs (Additional file 13: Table  S5 ). The "translation" GO term uncovered enrichment for several ribosomal proteins also known to play an essential role in cell proliferation in response to ribosomal stress  [60]. We also identified enrichment in molecular functions such as transferase activity indicative of possible post-transcriptional regulation differences between the two sexes. Female-biased genes in the third legs were enriched in various functions such as transcription factor, kinase, or GTPase activities that are probably involved in regulating biological processes such as transcription, metabolism, or signal transduction (Additional file 13: Table  S5 ).
Conclusions We identified a signature of leg exaggeration among sexbiased genes. Consistent with studies of sex-biased gene expression  [5, 68, 74, 75] , we found that the degree of sexual dimorphism in leg length is consistent with different patterns of expression among these genes. In our dataset, the most exaggerated legs mobilized more differentially expressed genes between the sexes and a higher degree of differential expression, especially in male-biased genes. Male-biased genes were significantly fast-evolving whereas female-biased genes were significantly slow evolving compared to unbiased genes. Interestingly, the same genes showed a highly similar pattern of sequence evolution in a sample of five additional species. We found that a large proportion of sex-biased genes, especially in the third legs, displayed also tissuebiased expression (Fig.
These annotations were based on the leg transcriptomic dataset generated in this study (36 samples in total), a transcriptome from whole-body individuals collected at all developmental stages (1 sample), and a transcriptome from a third inbred population not mentioned in this study (18 samples). Braker and Maker pipelines also performed de novo automatic annotations. Maker and Stringtie annotations yielded lower BUSCO quality and manual quality assessment using JBrowse revealed a relatively high number of gene fragmentations that were poorly supported by the alignments. We therefore used Braker annotation for further analyses (Additional file 2: Figure  S1 ). For Braker annotation, we used Hisat2 alignment files from each transcriptomic sample to train Augustus with UTR option. Final annotation includes 26,130 genes and 27,553 transcripts.
Transcriptome assembly, mapping and normalization Read quality was assessed with FASTQC version 0. 10. 1 (http://www. bioinformatics. babraham. ac. uk/projects/ download. html), and trimmed with TRIMMO-MATIC version 0. 32. Specifically, reads were trimmed if the sliding window average Phred score over four bases was < 15, and only reads with a minimum length of 36 bp were kept. Braker annotation was used as a reference for read alignment and the transcriptome quantification. We obtained around 90% alignment rate on the genome and about 72% of uniquely mapped reads using the Hisat2 method (Additional file 14: Table  S6 ). The latter condition was used for the estimation of transcript abundances and the creation of count tables (raw counts, FPKM and TPM tables) were performed using the StringTie pipeline (Additional file 15: Table  S7 )  [82, 83].
Identification of sex-biased genes We first filtered transcripts for which expression was lower than 2 FPKM in more than half of the samples after combining the two inbred populations (12 samples in total). Transcripts with average expression that was lower than 2 FPKM in both males and females were also discarded. The number of reads per "gene" was used to quantify differences in expression among the different conditions of interest using DESeq2  [85]. DESeq2 script is available from Dryad Digital Repository link below and was implemented from the DESeq2 vignette: https://datadryad. org/stash/share/TieHWUYVZWsPHBeCkDkbQ-b_0B3 NAsAFy6SUkD4ybrs. Differential expression analyses between males and females were performed on the two lines combined as we aimed to identify genes involved in male third leg exaggeration, which is a common feature to both lines.
Hierarchical clustering Average expressions of sex-biased genes in the different tissues were clustered using Euclidean clustering in the R package PVCLUST version 1. 3-2  [86]  with 1000 bootstrap resampling. Heatmaps and clustering were performed using the log2(TPM) average expression of each gene from each tissue. Heatmaps were generated using the R package GPLOTS version 3. 0. 1. 1.
Estimation of sequence evolution using dN/dS Whole transcriptomes of five Microvelia species (M. sp. (Cayenne), M. pulchella, M. paludicola, M. Ayacuchana, and M. americana) were sequenced and assembled as in  [75]. Additionally, gene sequences for Microvelia longipes were extracted from genome-based transcriptome assembly (see the above section). Protein sequences for all transcripts in these Microvelia species were retrieved and BLASTP results were used to construct Reciprocal Best Hits (RBH) clusters that include reciprocal best hits between Microvelia longipes, Microvelia pulchella, and at least a third Microvelia species. To improve the accuracy of the alignments we applied a simple length ratio cut-off to assign RBH of 0. 5. This filter prevents short transcripts generated by Trinity containing a very well conserved motif to be assigned as RBH if they are less than half the size of the longest sequence in the cluster.
Our dataset contained a total of 6289 RBH clusters that were further used to reconstruct the phylogenetic tree. To do so, gene sequences were aligned using PRANK (https://pubmed. ncbi. nlm. nih. gov/24170401/) and GBLOCKS  [87, 88]  and a tree was built using IQTREE  [89] [90] [91] [92]. The tree obtained was then used to calculate corrected dN/dS using the method described in  [76]. For dN/dS calculation we use all RBH clusters, aligned with PRANK (-t=guidetree. txt -f=phylips -prunetree -prunedata -translate -F -once -maxbranches = 0. 15) using the appropriate pruned guide tree plus GBLOCKS (-t=c -b5=h ; data available in Dryad through the link: https://datadryad. org/stash/share/ TieHWUYVZWsPHBeCkDkbQ-b_0B3NAsAFy6SUkD4 ybrs. dN and dS values for leafs were extracted using newick tools. Finally, we performed Wilcoxon tests on dN/dS values to compare the sequence evolution in sex-biased versus unbiased genes.
Fold-change reassignment and gene position From the DESeq2 analyses, all expressed genes were associated with a log2 fold change (Log2FC) and a p value (Padj). Unexpressed genes (FPKM < 2) were assigned a log2FC of 0 and a p value of 1. Among the expressed genes, we switched the log2FC to 0 for the unbiased genes (Padj > 0. 05), in order to directly assess sex-biased genes based on log2FC values (Additional file 17: Figure  10A ). In a second step, we merged the dataset on sex-biased expression with the gene positions (Additional file 17: Figure  S10A ).
Acknowledgements We thank  Jean-Nicolas Volff  for comments on the manuscript,  Laurent Duret  for help with dN/dS analyses and comments on the manuscript,  François Leulier ,  Kevin Parsons ,  Gaël Yvert  and  Augustin Le Bouquin  for helpful discussions,  Juliette Mends  for help with insect care. We thank  Lois Taulelle  and  Hervé Gilquin  for providing access to computing resources in the  Pôle Scientifique de Modélisation Numérique (PSMN) at the ENS Lyon.
Availability of data and materials All data generated or analyzed during this study are included in this published article, its supplementary information files, and publicly available repositories. -BioProject ID: PRJNA610161 and PRJNA642372. -Dryad link to dN/dS data analyses and scripts employed in this study: https://datadryad. org/stash/share/TieHWUYVZWsPHBeCkDkbQ-b_0B3 NAsAFy6SUkD4ybrs.
Phases mass fractions The 2D diffraction images recorded during the experiments were integrated all around the rings using PyFAI software  [18]. The 2D detector to sample distance and the detector tilts were calibrated using a cerium dioxide powder diffraction standard from NIST (CeO 2 -SRM 674b). The obtained 1D diffractograms (Intensity vs 2 Ө) were corrected from the instrumental contribution, using the same powder diffraction standard, and analyzed with a full Rietveld refinement procedure using MAUD software (Material Analysis Using Diffraction)  [19]. After data processing, the Rietveld refinement allowed to determine the evolution of the mass fractions of the phases present in the alloy. The absolute uncertainties on the measured mass fraction was 2 wt. %. The carbides mass fractions determined by Rietveld analysis allowed the calculation of the carbon mass fraction trapped in carbides using Eq.
(  8 ) ). The stress analysis was performed by applying the sin 2 method. After the calibration procedure made on a cerium dioxide powder diffraction standard (CeO 2 -SRM 674b), the recorded 2D diffraction images were integrated around the azimuthal angle δ along the Debye-Scherrer ring over 2 °sectors using pyFAI software  [18]. In order to determine the residual stresses in the alloy, the variation of the inter planar distance d hkl in different directions should be determined. So, at least the directions dependent positions 2 Ө of one plane of each phase must be evaluated. The austenite ( γ Fe ) (220) and M 23 C 6 (420) peaks were selected as they do not overlap with peaks of other phases. In the zone where M 7 C 3 precipitated, no residual stress determination was carried out because the peaks of M 7 C 3 overlapped with peaks of other phases (austenite and M 23 C 6 mainly).
The mechanical effect of TiC carbide was neglected as its mass fraction does not exceed 2%, see § 4. 1. The peaks line profiles were fitted using a Pearson VII function. An automatic procedure was developed using Python environment to analyze the data. After data processing, 2 Ө positions, full width at half maximum (FWHM), intensity, shape factor, background parameters and residual stresses were extracted. The FWHM is sensitive to stress-strain accumulation (dislocation density), microstrains (strain heterogeneities between and inside grains) and microstructural evolution (essentially chemical gradient in our case). In order to consider the uncertainties in the 2 Ө position due to the variations of the beam position during experiments and to free ourselves of the exact knowledge of the Debye-Scherer ring center, an average 2 Ө position for the opposite azimuths ( δ and δ + 180 °) was used in the residual stress determination  [ 20 , 21 ].
That remains reasonable in our case. Jiang et al. [51]  have mentioned a slight variation of the Young's modulus of a 316 L austenitic stainless steel between 195 GPa and 205 GPa for a variation of an amount of carbon dissolved in the austenite between 0. 03 and 1. 4 wt. %. This Young's modulus increase will induce a variation of 100 MPa in the value of residual stress calculated for the highest strain level which was about 3. 5 GPa. This 100 MPa increase was lower than the experimental uncertainty given on this value. The cutting of the measured sample from the carburized plate can lead to residual stress relaxation. That effect was evaluated by FEM simulations using Cast3M software  [52]. A stress relaxation due to sample cutting was observed but the residual stress profile within the depth of the sample was not affected.
Acknowledgments The authors gratefully acknowledge the  Deutsches Elektronen-Synchrotron  ( DESY-Petra III, Hamburg, Germany ) for provision of beamtime at the PETRA P07-EH2 beamline. We would like to thank  Olof Gutowski  for assistance during the experiments and  Professor Dominique Poquillon  for the FE simulations. The  RG4  project from  CEA  is thanked for having partially funded this study.
Funding This work was supported by the  French Alternative Energies and Atomic Energy Commission  ,  EDF ,  Framatome , the  project CALIPSOplus  under the Grant Agreement 730,872 from the  EU   Framework Programme for Research and Innovation HORIZON 2020  and the  French State through the program  "Investment in the future" operated by the  National Research Agency (ANR)  and referenced by  ANR-11-LABX-0 0 08-01 [58 ].
Let us assume we have 3 nodes as depicted in Fig. 3. Considering the range mismatch, node 1 is connected to node 2 through the WuR but it is not connected to node 3. On the contrary, node 1 is connected to all the nodes through the main radio. In other words, there are two parallel topologies: the one defined by the WuR links (very short range) and the other one defined by the main radio links (medium range). In that example, if node 3 wants to send a message to node 1, it cannot do it using WuR because there is no direct link on the WuR channel between them to send a wake-up signal and wake it up. However, if node 2 serves as a relay for the WuS, then node 3 can communicate data with node 1, as depicted in Fig. 4. Node 3 first sends a wake-up signal to node 2 (step 1). Upon reception, node 2 forwards this wake-up signal to node 1 and keeps its main radio in sleep (step 2).
Then, node 1 wakes up its main radio and starts listening to the channel waiting for the data frame. Finally, node 3 transmits the data (step 3). From now on we define the amount of time between step 1 and step 3 as the sync delay because node 3 is waiting for node 1 to wake up. Node 1 may reply with an acknowledgment frame on the main radio (not shown in the figure). 1 -55 dBm for the WuR against -90 dBm to -110 dBm for the main radio. This means that the nodes need to find routes on the WuR topology to communicate with some of their main radio neighbors. This is what we understand by wake-up signal routing or the routing layer in wakeup radios. For this reason, we need to choose a strategy to route the wake-up signal on the WuR topology.
The WuR is always-on, so all the WuR neighbors will receive this WuS. Every WuR neighbor that receives this WuS will forward it only once, except for the destination. When the latter one receives the WuS it wakes up its main radio and waits for the data frame. Finally, the source transmits the data frame on the main radio channel. A timeline for this process is shown in Fig. 6. We showed in previous works  [25]  that a clear-channel assessment function is required to limit collisions when transmitting the WuS. Such a mechanism is included in this work. In this work, we limit the network size such that all the nodes are 1-hop neighbors on the main radio. In the future, we plan to extend this protocol to larger networks where the main source and main destination nodes are located multiple hops away on the main radio.
WuS size WuR uses a low data rate which translates into a long time over the air occupying the medium. For that reason, the size of the WuS message must be as small as possible. The WuS requires one field for the address of the destination. Typically, in the WuR literature, an address is described with 8 bits, so we can address up to 255 nodes in a flat address scheme. We set the hop-count field to 2 bits. Such value allows building routes including up to 5 nodes (including source and destination). We showed in  [10]  that the performance of WuR-based MAC protocols seriously degrades when the source requires more than 5 hops of the WuS to reach the destination. That makes a total of 10 bits in this protocol. Notice that the maximum number of hops of the WuS places a constraint on the WuR hardware module design but only to take advantage of the full range of the main radio.
Algorithm REFLOOD operates according to the pseudocode shown in Algorithm 1. The transmission process waits until it is signaled by upper layers. In the case of a source node, such a signal is a trigger for data transmission. First, if there is a flood in progress, the WuR module is blocked, and the process waits until it becomes available. Then, the frame for the WuS is prepared for the WuS transmission with the address of the destination and the maximum extension of the flood,  given by ℎ. In our implementation, the value for this parameter is 4, since we use 2 bits for the hop-count field, for the reasons explained in Section 4. 2, based on  [10]. Next, the flood is started by transmitting the WuS. Once the flood is done successfully (when the function Flood(WuS) returns ''success''), the data frame is sent on the main radio.
Second, to avoid creating new flood processes, triggered by other concurrent requests to transmit data from upper layers, that could generate collisions in the medium. The pseudocode used to block the WuR module is shown in Algorithm 3. The function Block_WuR(delay, event) takes two arguments as inputs: the interval of time during which the WuR will remain blocked, and an optional event that can stop the wait prematurely. In the procedure, the function disables the WuR module interrupts to ignore the reception of WuS. Then, a timer is used to wait for the specified interval. Finally, once the timer expires or the optional event is issued, the WuR interrupts are re-enabled, and a signal is generated. Such signal notifies any potential process which may be waiting for the WuR module to acquire it, particularly WuR_send_process(), described previously in Algorithm 1.
If there is a match, it is the case of a destination node. Then, it turns on the main radio and waits for the data frame, while blocking the WuR module to avoid the aforementioned problems (infinite flooding and creating new flood processes). Once a period given by  _ has passed or the data frame has been received, the main radio is turned off and the process continues. On the other hand, if the destination field address does not match the address of the node, the hop count field in the WuS is checked. If such field is zero, the WuS is dropped and the Algorithm 5: WuR_receive_process() process continues, to limit the extension of the flood. Otherwise, the node decrements such field and forwards the WuS, contributing to the flood. 1.
Then it sends a DIO to its neighborhood on the main radio (step 2) which allows nodes 2 and 3 to associate to the RPL DODAG and compute their ranks. Notice that the control packets are transmitted on the main radio but with reduced transmission power so that the range matches that of the WuR. Then, these nodes will generate their own DIOs and send them to their WuR neighbors using the same process (first sending a WuS to their WuR neighbors in step 3 and then transmitting the DIO on the main radio in step 4). The resulting DODAG is depicted in Fig. 8 , which shows the extending routing tables associated with each node. When node 4 has a data packet to send, it uses this DODAG to reach node 1 in the same way as in the example of Fig. 4. A timeline of this process is shown in Fig. 9. First, the source sends a WuS to node 2.
Finally, WuR represents the power consumption of the WuR module and is the aggregation of its transmission, reception, and listening modes (when the WuR is in listening mode, the WuR microcontroller is sleeping). In this plot, we can see that the state that contributes the most to the overall power consumption in ContikiMAC is the main radio, for all traffic loads. Such observation is in line with the literature  [3]. The main radio power consumption is significant because of the periodic idle listening which is independent of the traffic load. On the contrary, in PROPL, the contribution of the main radio is only significant in high traffic scenarios because of the rate of control packets sent by RPL which is higher in such cases, given that the elapsed time of the simulation is shorter. In contrast, such contribution in REFLOOD is not significant in any traffic load because it does not require control overhead on the main radio.