dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequence assembly and analysis For both genomes, reads were then assembled into contigs using the assembler Hifiasm v0. 13-r307 with aggressive duplicate purging enabled (option -l 2)  [19]. The primary contig assembly was used for all downstream analyses. Genome contiguity was measured using assembly_stats. py [20]  and genome completeness was determined using Busco v. 5. 2. 2  [21]  and the obd10 reference Endopterygota. Contamination in the genome was assessed by creating Taxon-annotated GC-coverage (TAGC) plots using BlobTools v1. 0  [22]. First, assemblies were indexed using samtools faidx then HiFi reads were mapped back to the indexed assemblies using minimap2  [23]  with -ax asm20. The resulting bam files were sorted with samtools sort. Taxonomic assignment was performed via Megablast and using the NCBI nucleotide database with parameters -outfmt 6 qseqid staxids bitscore std' -max_target_seqs 1 -max_hsps 1-e value 1e-25.
For EST evidence, we used the transcriptome of Ptilostomis semifasciata (111015_I297_FCD05HRACXX_ L1_INSbttTHRAAPEI-17, 1kite. org) for E. regina and Iso-seq data for P. interpunctella. Evidence used in Maker and the Maker config files can be found in the Gigascience GigaDB repository  [26]. To add functional annotations to the predicted proteins, we blasted the predicted proteins against the ncbi-blast protein database using BlastP in blast. 2. 9 with an e-value cutoff of 10 -4 and -max_target_seqs set to 10 (see repository). We then used the command line version of Blast2GO v. 1. 4. 4  [37]  to assign functional annotation and GO terms.
Structural and functional annotation A total of 56. 26% of the E. regina genome was classified as repetitive (54. 2% interspersed repeats). More than half of the interspersed repeats, 29. 87%, could not be classified by comparison with known repeat databases, and therefore may be specific for Trichoptera. Of the repeats that were classified, retroelements were the most abundant, comprising 15. 35% (of which 14. 55% are LINEs) of the genome. The relatively high proportion of repetitive sequence supports previous studies which suggest that repetitive element expansion occurred in lineages of tube case-making caddisflies, such as the closely related genera Agrypnia and Hesperophylax  [9, 35]. In contrast, a total of 31. 94% of the P. interpunctella genome assembly was masked as repeats. A total of 23.
04% of the annotated repeats were interspersed repeats. Details on the repeat classes are given in the Gigascience GigaDB repository  [26]. The genome annotations resulted in the prediction of 16,937 and 60,686 proteins in P. interpunctella and E. regina, respectively. Of the annotated proteins, for E. regina,  28, 358  showed significant sequence similarity to entries in the NCBI nr database, of those 12,550 were mapped to GO terms, and 5,652 were functionally annotated with Blast2GO. For P. interpunctella, 16,349 were verified by BLAST, 12,410 were mapped to GO terms, and 9,711 were functionally annotated in Blast2GO. The major biological process found in the two genomes were cellular (E. regina: 2,326 genes; P. interpunctella: 4,725 genes) and metabolic (E. regina: 2,454 genes; P. interpunctella: 3,699 genes) processes.
Re-use potential We provide a complete genome of two species of silk-producing insects in the superorder Amphiesmenoptera, the moth P. interpunctella and the caddisfly E. regina, and recover the difficult-to-sequence repetitive regions of both genomes with HiFi sequencing. P. interpunctella is currently being developed in multiple labs as a model organism and this genome assembly will facilitate molecular genetics research on this species. We show that PacBio HiFi sequencing allows for accurate generation of repetitive protein-coding regions of the genome (silk fibroins), and this likely applies to other similarly repetitive regions of the genome. For Trichoptera, there are only four other HiFi genome assemblies available on Genbank, only one of which has been published  [38]  and insects have generally been neglected (relative to their total species diversity) with respect to genome sequencing efforts  [15] [16] , which is especially true for aquatic insects  [14].
Acknowledgements We thank the  BYU, UF and LOEWE-Centre for Translational Biodiversity Genomics (TBG) High Performance Computing clusters  for providing the computational resources needed to complete this study. TBG is funded by the  Hessen State Ministry of Higher Education, Research and the Arts (HMWK) , which financially supported J. H. and S. U. P. S. H. was supported by  NSF  award # OPP-1906015.
Contrary to CT images, digital images exhibit disturbances like dirt or sawing marks which make the analysis harder. Some images of our dataset are shown in Figure  1. The dataset is split into 2 subsets: Besle imageset and BBF imageset. Both subsets include the same log ends shot with different lighting conditions (see Section 5. 1 for more details). In this context, Norell et al. [16] , Schraml et al. [17]  and Kurdthongmee et al. [12]  have proposed algorithms for pith detection on digital images of untreated wood cross-sections. Recently, Deep Neural Networks (DNNs) have been trained in order to address this problem  [11]. On the other hand, state-of-the-art approaches based on RGB images rely on tree rings analysis. They can be described with three steps: 1. Estimate normals from tree ring local orientations.
• Then compile the project: make This procedure is also detailed in the provided README. md. The compilation creates an executable called AntColonyPith which can be used as follows:. /AntColonyPith --input=path_to_image  [list_of_parameters]. /AntColonyPith --input=path_to_image --parameters=path_to_parameters. json Some images are provided in the directory samples. Hereafter, several usage examples: To run the program on harvest. jpeg with default parameters. /AntColonyPith --input. /src/samples/harvest. jpeg To run the program on harvest. jpeg with 10 × 10 ants, α = 1. 0 and without animation. /AntColonyPith --input. /src/samples/harvest. jpeg --ant=10 --alpha=1. 0 --animated=false More details about the options are given in the command line helper. /AntColonyPith --help 5 Experiments and Results.
Dataset We evaluated our method with two different imagesets of Douglas Fir (see Figure  1 ). The first one (Besle) consists of 65 (4320 × 3240 pixels) log end images of Douglas fir. The images were captured just after harvesting in forest with a digital camera (Panasonic Model DMC-FZ45). The second one (BBF) consists of 40 (4608 × 3456 pixels) log end images of Douglas fir. The images were captured with a smartphone (Huawei Model ANE-LX1). Both datasets concern the same log ends. As we were interested in ambient light variations, dirt or sawing marks, we captured images as raw as possible. There are 11 logs, but each log has been taken several times (from 3 to 5 times) by slightly rotating or moving the camera between each view. In average, a pixel stands for 0. 191 mm, with a minimum at 0.
Experiments were performed on an Intel Core i7 with 2,5 GHz and 16GB RAM. To determine the best parameters' values, we minimized over the whole BBF dataset the sum of distances between ground truths and results. Then, we validated those values on the Besle dataset. Parameters for the ACO-based algorithms are shown in Table  1  and Table  2  (the values is the same for both runs unless otherwise indicated). One of the most important parameters is δ, the reduction factor of the image. It should be mentioned that the proposed method relies on tree rings analysis, and the information between tree rings is less important for the detection. Due to the high resolution of both imagesets, this resizing step allows to reduce information between tree rings (without removing them) and also the computation time.
Results Although no method in the literature has been designed to operate in the experimental conditions described in the previous section, we have compared our results with  [12] ,  [17]  and  [11]  on our datasets. Kurdthongmee et al. ,  [12]  divide the image into blocks and use Histogram of oriented Gradient (HoG) for the orientation estimate. Then, for each block, they accumulate a line according to the orientation with highest count of the histogram. Once the accumulation is finished, the pith is assumed to be the point with the highest accumulation. However, depending on where the block is located in the image certain orientations cannot be selected. For instance, if the block is in the upper left, then orientations π 4 , 5π 3 cannot be accumulated. We implemented the algorithm of Kurdthongmee et al.
,  [12]  and computed optimized parameters with a subregion of size 24 × 24 pixels and a quantization factor of 12. We also compared with Schraml and Uhl algorithm  [17]  using optimized parameters for our datasets. The method consists in dividing the image into blocks (which may overlap) and using Fourier analysis for the orientation estimate. Then it accumulates for each block a line according to the orientation estimate. For the comparison with the DNN  [11] , we have performed a twofold cross-validation. For each imageset, half of the images have been used for the training and the other half for the validation. Two models were trained for each imageset by inverting the training and the validation sets. Ground truths consisted of a square of 300 × 300 pixels with the pith position at the center.
As we have few images, data augmentation has been applied on-the-flight (i. e. each time each image was transformed before passing through the DNN). The transformations related to data augmentation are shift (vertical and horizontal), shear and zoom. The DNN hyperparameters are the same as in  [11] , only the input size has been modified, being 576 × 432 for both imagesets. The ratio is kept for each imageset and the ground truth is also resized according to that. The DNN returns a box with a probability of finding a pith on it. The predicted pith is the center of the box with the highest probability. To compare each method, we have aggregated all the predictions from the trained models (which gives us predictions for all images) and we have computed scores on this aggregation. Table  5  presents a statistical analysis of the three algorithms on our datasets.
The deep learning method is the fastest. The drawbacks are the learning time and the need to create huge datasets with ground truths. Our method is on average 5 times faster than  [17]  and can be easily parallelized. We can observe that both our method and  [17]  are more accurate than  [11, 12]. The results from  [11]  are worse on the BBF imageset, this may be due to the small number of images in it. [17, 12, 11]  (in mm) and average time to process one image (in ms). Figure  8  presents boxplots for our method,  [17]  and  [11]  to better illustrate the differences between them. We excluded  [12]  since their results are less accurate than the three others. For the Besle imageset, our method is a little less accurate than  [17]. The DNN  [11]  is even a little less accurate and presents one outlier (7.
Conclusion In this paper, we proposed a method to estimate pith location on RGB images based on an ACO algorithm. The raw images were processed directly with no prior segmentation nor cropping. To the best of our knowledge, no method has been developed in such conditions. Despite this, we compared our method with other algorithms. Experiments were performed on two datasets of log end images   9 : Two examples where the methods failed to be below 5mm from the ground truth. The ground truth is the white cross, results obtained by DNN  [11]  is the green one, Schraml and Uhl  [17]  is the blue one, and our method is the red one. of Douglas fir. The results showed a very accurate estimation. Our method is as accurate as other methods although we did not make any assumptions on the image. Furthermore, results showed a computational time low enough to be used in sawmill environment or in forest (with smartphones).
Methods Admission CT scans of 57 patients with unilateral cSDH from two university hospitals were retrospectively analyzed (n = 34 from the University Medical Center Hamburg-Eppendorf, Hamburg, Germany and n = 23 from the University at Buffalo, Buffalo, NY, USA). This study was approved by the clinical ethics committee of the city of Hamburg medical board. The ethics committee waived the need for patient consent, since only anonymized data was used. Axial image data sets with a slice thickness of 4 or 5 mm were evaluated for each patient by a total of four raters with 15, 10, 4 and 8 years experience in neuroradiology. The MLS was measured in a total of four locations, foramen of Monro (FM), thalamus (Th), mid septum pellucidum (SP), location of maximum overall MLS (max) with the following two different techniques: Displacement perpendicular to anatomical midline (MLS-M), and displace-K Fig.
Statistics Statistical analyses were performed using the software R (R Statistics ® version 4. 0. 2; R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2018, URL https://www. R-project. org/). Univariable distribution of metric variables is described by mean and standard deviation (SD). For categorical data, absolute and relative frequencies are given. Interrater reliability for each MLS measurement technique and location was evaluated by calculating the intraclass correlation coefficient (ICC) using the psych package  [19] , while an ICC > 0. 9 was interpreted as excellent agreement  [20]. In clinical trials with specific study standards, a constant quality control is required to guarantee high interrater reliability.
Data analysis. The ETS activity in northern Cascadia is well known to occur beneath the onshore area (Fig. 1a ) where the GNSS network has been deployed (Figs. 2 and S1-S3)  [4] [5] [6] [7] [8] [9] [10] [11] [12]. An inversion of the daily GPS data has revealed that the March to April 2017 Cascadia SSE migrated from ~ 48. 0 N towards the northwest along the fault-strike with roughly concordant migration of tremors  9. We obtained kinematic subdaily positions at sites in northern Cascadia by processing the raw GPS observables at an interval of 30 s and subsequently performed post-processing to retrieve the transient deformation signature likely due to the SSE. By correcting the 30-s kinematic coordinates for temporally  [32] [33] [34] [35]  and spatially  36  correlated errors as well as some outliers, we finally obtained 30-min interval coordinate series at 64 sites (see "Methods") (Fig.
4a, c ,e, 5 and 6 and Videos S1 and S2) (see "Methods") which provides the reasonable fit to the data (Figs. 2  and 3 ). The final moment magnitude (M w ) for the entire period is 6. 5 (Fig. 4a ). The moment rate history of the SSE has two peaks in the 30-min data inversion, consistent with two peaks of daily tremor counts (Fig. 5 ). We divided the inferred spatiotemporal slip evolution using the 30-min data into four stages based on the moment rate history (Fig. 5 ). We determined the initiation and termination of Stage 1 and Stage 4, respectively,. Displacements using the 30-min data (red) with the prediction of the 30-min spatiotemporal slip model (black) during four stages as labelled. The displacements in each stage for the 30-min data are obtained as the difference of the initial and the final positions, which are defined as the average positions during the 2 days centring at the first and the last epoch of each stage, respectively.
Outliers deviating from the average by more than 3 times of the standard deviation were removed when taking the averages. Prior to the displacement computation, the 30-min time series were corrected for whole network translation estimated in the slip inversion. The model displacements are also obtained by the averaging using the same time window but without the outlier removal. based on daily tremor counts (Fig. 5 ), because our data post-processing required to assume that no SSEs occur when no tremors are detected (see "Methods"). In the first stage, our model shows slow crack-like isotropic growth of the SSE at 30-50-km depth with little migration (Stage 1; Figs. 4c and 6a and Video S2). This depth range is typical of short-term SSE occurrence in this region  1, [4] [5] [6]. The 30-min data does not exhibit the clear trenchward motion pattern during this stage, which is due to the poor signal-to-noise ratio of the trench-normal component.
S7b ). Here, the diurnal variation was estimated as the seasonal component, and we used the estimated trend and residual components for subsequent analysis to avoid dropping any short-term transient left in the residual component. Then, we decimated the 30-s interval position time series into a 30-min interval (blue and pink time series in Fig. S7b ) after applying the low-pass filter with a cut-off period of 1 h (yellow time series in Fig. S7b ). This decimation was required to reduce computational burden for the subsequent slip inversion. As the time series to be filtered contain the permanent offset due mainly to the SSE, we applied a Hanning window with a width of 1 day at both ends of the time series (i. e. DOYs 073 and 103) prior to low-pass filtering using Seismic Analysis Code (SAC)  54, 55.
Acknowledgements We acknowledge  T. Herring  and  S. Shimada  for their technical assistance in using TRACK and  S. Li  for providing his research outcome 38. We used Generic Mapping Tools  62  to draft the figures. This manuscript has been improved by constructive comments by the editor  Y. Yagi  and two anonymous reviewers. This study is supported by the  Japan Society of the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI)   JP21K14007  and the  Seto Prize of the Geodetic Society of Japan (Y. I. )  and the  Director Discretionary Fund of Earthquake Research Institute , the  University of Tokyo  ( Fiscal Year 2020 ) (Y. I. , Y. A. , J. F. ). The first author (Y. I. ) is a  JSPS Overseas Research Fellow. We used the computer systems of the Earthquake and  Volcano Information Center of the Earthquake Research Institute , the  University of Tokyo.
Data availability The RINEX files were retrieved via UNAVCO (https:// data. unavco. org/ archi ve/ gnss/ rinex/ obs/) and the Western Canada Deformation Array (WCDA; ftp:// wcda. pgc. nrcan. gc. ca) servers. FES2004 ocean tide model is available at http:// holt. oso. chalm ers. se/ loadi ng/. TRACK in GAMIT/GLOBK program  [44] [45] [46]  and Seismic Analysis Code (SAC)  54, 55  are available upon request to the developers at http:// geoweb. mit. edu/ gg/ licen se. php and http:// ds. iris. edu/ ds/ nodes/ dmc/ forms/ sac/, respectively. We made our slip and slip rate files available at Zenodo as Itoh et al. 63.
30 d 3 303 Figure 3. Displacements using the 30-min data (red) with the prediction of the 30-min spatiotemporal slip model (black) during four stages as labelled. The displacements in each stage for the 30-min data are obtained as the difference of the initial and the final positions, which are defined as the average positions during the 2 days centring at the first and the last epoch of each stage, respectively. Outliers deviating from the average by more than 3 times of the standard deviation were removed when taking the averages. Prior to the displacement computation, the 30-min time series were corrected for whole network translation estimated in the slip inversion. The model displacements are also obtained by the averaging using the same time window but without the outlier removal.
Open Field Test Anxiety-like behaviours and locomotor activity were assessed in the open field consisting of a 60 × 70 × 30 cm square arena. A virtual zone (20 × 23 cm) was delimited in the center of the arena. Mice were placed facing the center of the arena and allowed to freely explore for 10 min. After each observation, the arena was cleaned with 30% ethyl alcohol in water to remove possible scent cues left by the animal. The time spent at the center and periphery of the open field and total distance travelled were recorded using the video tracking system AnyMaze and analyzed using Smart 2. 5 software.
Typically, two out of the six (dorsal-central) slices were used for LTP experiments. Electrophysiological recordings: Before transferring the slice to the recording chamber, the CA1 area was disconnected from the CA3 area by a surgical cut. The slice was then placed on a nylon mesh, completely submerged in a recording chamber and continuously superfused (2 mL min-1) with oxygenated ACSF at 32-33 • C. Slices were incubated for 15 min in the recording chamber before initiating electrical stimulation. Synaptic responses of CA1 pyramidal neurons apical dendrites were elicited by stimulation of the Schaffer collateral/commissural pathway. Stimulation pulses (80 µs duration; 30 s interpulse interval), triggered by a PC controlled by WinLTP software  [41]  were delivered by a stimulus isolation unit (DS2, Digitimer, Welwyn Garden City, UK) through a concentric bipolar Pt-Ir electrode (125 µm/Rnd/25 µm; FHC, Bowdoin, ME, USA).
Evoked potentials were recorded with glass electrodes filled with 150 mM NaCl (2-10 MΩ resistance) placed in the distal third of the stratum radiatum to record field excitatory postsynaptic potentials (fEPSP). The distance between the stimulating and recording electrodes was 300-400 µm. Recorded potentials were amplified with a Neurolog NL 104 amplifier (Digitimer), digitized with the sampling rate of 50 kHz (Digidata 1322A, Molecular Devices, Foster City, CA, USA) and stored in a PC for offline analysis using WinLTP software. At the beginning of each experiment, a stimulus-response curve (SRC), obtained by gradually increasing stimulus intensity, was recorded. The fEPSP was measured as the slope of the initial falling phase of the response recorded in the stratum radiatum following the afferent volley and measured by linear regression in the region between 30 and 70% of the fEPSP amplitude.
After sample application, the extraction plate was washed with 10% MeOH (1 mL). After drying under aspiration, lipid mediators were eluted with 1 mL of MeOH. Prior to LC-MS/MS analysis, samples were evaporated under nitrogen gas and reconstituted in 10 µL of MeOH. LC-MS/MS analyses of eicosanoids were performed as previously described  [30]. Briefly, lipid mediators were separated on a ZorBAX SB-C18 column (2. 1 mm, 100 mm, 1. 8 µm) (Agilent Technologies, Santa Clara, CA, USA) using Agilent 1290 Infinity HPLC system (Technologies) coupled to an ESI-triple quadruple G6460 mass spectrometer (Agilent Technologies, Santa Clara, CA, USA). Data were acquired in the Multiple Reaction Monitoring (MRM) mode with optimized conditions (ion optics and collision energy). Peak detection, integration and quantitative analysis were done using Mass Hunter Quantitative analysis software (Agilent Technologies, Santa Clara, CA, USA) based on calibration lines built with commercially available eicosanoids standards (Cayman Chemicals, Ann Arbor, MI, USA).
The V3-V4 hypervariable region of the 16S rRNA gene was amplified from the DNA extracts and prepared for sequencing using the Illumina 16S Metagenomic Sequencing Library Protocol (Amplicon). A detailed description of the 16S rRNA sequencing protocol can be found in the Supplementary Methods. Microbiome Pre-processing: Three hundred base paired-end reads were pre-filtered based on a quality score threshold of >28, trimmed and filtered for quality and chimaeras using the DADA2 library  [46]  in R (version 3. 6. 3). Only samples with >10,000 reads after quality control steps were used in the analysis. Taxonomy was assigned using the DADA2 package that included the SILVA SSURef database release v132. Parameters as recommended in the DADA2 manual were adhered to unless mentioned otherwise. ASVs were aggregated at genus level; those that were unknown at the genus level were not considered in the downstream analysis.