dataesr/scientific-paragraphs-categorization huggingface dataset:
4. 0. 3). The ASVs accumulation curves were drawn using the 'rarecurve' function in the vegan package (v. 2. 5. 7)  63. Observed richness and Shannon diversity indexes were calculated using the 'estimate_richness' function in the phyloseq package (v. 1. 32. 0)  64. Alpha diversity analyses were based on a rarefied dataset (30,845 reads per sample) in order to remove biases linked to uneven sequencing depth. Since our data were not normally distributed, nonparametric tests were applied. The significance of the changes in the alpha diversity parameters were tested using Mann Whitney Wilcoxon tests and the 'stat_compare_means' function in the ggpubr package (v. 0. 2. 4). Bray-Curtis distances calculated on Hellinger-transformed data were used to determine shifts in the gut bacterial community using a PCoA ordination.
The effect of PAH exposure and parasite infection were tested using permutational analyses of variance (PERMANOVA) with the vegan package. The function 'varpart' from the vegan package was used to quantify the relative, joint, and unique contribution of predictor variables to explain bacterial community compositional variations between groups. Changes in bacterial community composition were further explored at the phylum and genus levels. Differentially abundant bacterial taxa between test groups were identified using Mann Whitney Wilcoxon tests. To correct for multiple comparisons, a Benjamini-Hochberg False Discovery Rate adjustment was used (cut-off value: 0. 05).
Funding This work was financially supported by the  École pratique des hautes études (EPHE)  for experimental equipment and CNRS UMR 7619 METIS for microbial analysis. N. Molbert was funded by a PhD grant from  Sorbonne University Environmental Transition Institute. This work benefited from technical and human resources provided by  CEREEP-Ecotron Ile-De-France  ( CNRS/ENS UMS 3194 ).
Data availability The raw 16S rRNA gene sequences generated during this study were deposited in the European Nucleotide Archive (http:// www. ebi. ac. uk/ ena/ data/ view/ PRJEB 48108). The R code used to process the amplicon sequences, generate the phyloseq object, and produce all the figures, tables and statistical analyses is available from Github at the URL https:// github. com/ ycoli n26/ chub_ pahs_ paras ite.
Acknowledgments We would like to thank  Gang Li  for the generator samples of the SM background and the full simulation study of the CEPC baseline b-tagging. We also thank  Yudong Wang  for the signal sample generation, and  Xu-Chang Zheng  for discussions on B meson productions. We are grateful to  Shan Cheng  and  Chunhui Chen  for their beneficial discussions on the status of B → ππ study. This project is supported by the  Institute of High Energy Physics, Chinese Academy of Sciences  ( E2545AU210 ,  E15152U110 ). It has also received the support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skodowska-Curie  grant agreement No 860881 -HIDDeN. L. Li  is supported by the  DOE  grant  DE-SC-0010010.
2017). Lastly, participants underscored the benefit of supporting and sustaining fungal culture collections and databases given their value in enabling natural product discovery and fungal research more broadly. Can we harness fungi to develop clean fuels that help avert climate catastrophe? Fungi have phenomenal potential for applications in bioremediation  (Harms et al. 2011; Kumar and Chandra 2020)  and in the production of sustainable energy sources, such as in the biofuel industry and advanced biorefineries  (Hong and Nielsen 2012; Srivastava et al. 2018; Keasling et al. 2021). Fungi and fungal enzymes are being explored for their ability to convert renewable lignocellulosic materials (plant dry matter) into biofuels, providing environmentally friendly and sustainable alternatives to fossil-derived fuels and chemicals  (Srivastava et al.
Recommendations The 2007  (Buckley 2007 ) and 2017 (American Academy of Microbiology 2019) AAM colloquia reports on the fungal kingdom provided recommendations to promote advances in fungal research. These recommendations were revisited during the meeting hosted by CIFAR and the Burroughs Wellcome Fund and updated to reflect the current state of the world's fungi and to provide suggestions for accelerating the development of novel strategies to combat the threats posed by fungi and harness their extraordinary potential (Fig. 1 ). These recommendations have been proposed within the context of relevant background information in the main text and are further distilled in a succinct format below. genome sequences, have generated abundant and rich datasets. Compiling, storing, and annotating these data in an integrated format that is accessible to the community is critical to informing research and promoting advances.
Documenting the global burden of fungal disease would enable increased understanding of disease emergence, range expansion, and the impact of drug resistance. To achieve this objective, public, domestic animal, and wildlife health agencies could implement programs to transparently report cases of fungal disease, and crop monitoring services could report fungal disease surveillance data to publicly accessible databases. 8) Limit the Global Transport of Living Plants and Animals. Global trade can promote the spread of plant and animal fungal pathogens  (Fisher et al. 2020). Enhanced pathogen surveillance of internationally transported plants and animals coupled with stringent import regulations could limit the introduction of potential pathogens to naı ¨ve populations. Locally maintained, disease-free nursery and animal stocks would reduce the need for global import and help prevent the spread of fungal pathogens.
Acknowledgments We thank  CIFAR  and the  Burroughs Wellcome Fund  for their support and for hosting the " Future of Fungi " workshop. LEC and JH are Co-Directors and Fellows of the CIFAR program Fungal Kingdom: Threats & Opportunities. NARG and JWT are Advisory Committee Members of the CIFAR program Fungal Kingdom: Threats & Opportunities. DSB, CAC,  CRC ,  MCF , LKF-L, SJG, TYJ, HJ, BSK, JWK, DCS, JES,  EES , and GDW are Fellows of the CIFAR program Fungal Kingdom: Threats & Opportunities. IVE, ACG, and  RSS are CIFAR Azrieli Global Scholars of the CIFAR program Fungal Kingdom: Threats & Opportunities. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government. Funding NTC is supported by a  CIHR Canadian Graduate Scholarships-Doctoral  award.
DSS-treated colons of PC-deficient mice display elevated IL-6 signaling To gain insight into the molecular traits modulated by PC presence, we generated conditioned medium from distal colons taken from DSS-treated control and ColVIcre-IFT88 flx/flx animals and analyzed those by mass spectrometry employing LC-QTOF-MS/MS analysis  (Fig 5A). The supervised analysis identified 392 and 424 proteins in medium from DSS-treated colons of control and ColVIcre-IFT88 flx/flx mice, respectively (ProteomeXchange identifier pxd033440). Ingenuity Pathway Analysis revealed a prominent elevated activation of IL-6 as well as the connected EGF signaling  (Schumacher & Rose-John, 2019)   Given that a critical role for the prototypic pro-inflammatory cytokine IL-6 in DSS-induced colitis is well established  (Chassaing et al, 2014)  and concurring with the secretome analysis, we detected elevated Il-6 transcript levels in distal colons from DSS-treated ColVIcre-Kif3a flx/flx mice as compared to controls (Fig  5C ).
30-40% of 2P-CF starved in the control medium displayed PC, whereas the PC number on 2P-CF starved in inf-CM was consistently lower (Fig  6B ). We performed RNA-seq analysis of 2P-CF starved in inflammatory or in control medium to gain insight into the molecular traits of those cell cultures. Supervised analysis revealed 782 genes differentially regulated between two starvation conditions: 479 genes were upregulated (cluster 1) and 303 genes were downregulated (cluster 2) in the inflammatory condition compared with the control condition  (Fig 6C and GEO ID GSE207877). The top discriminant transcripts in the control setting included genes related to epithelial-mesenchymal transition (EMT) and Hedgehog canonical and noncanonical signaling, whereas those in the inflammatory condition included prominently genes associated with inflammatory response (GEO ID GSE207877).
The gene expression signature was then submitted to Gene Set Enrichment Analysis (GSEA) (http://www. broadinstitute. org/gsea/), in regard to the 50-hallmark gene sets from the Molecular Signatures Database (MSigDB). Strikingly, the most significantly upregulated pathway in the inflammatory condition was the IL-6-JAK-STAT3 gene set (Fig  6D ). In addition, the inflammatory condition upregulated the Il-6 transcript itself in the fibroblast cultures (GEO ID GSE207877), highlighting an active inflammatory loop. A Generation of primary CF cultures from Ift88 flx/flx mice, that is, control animals. CF were isolated as described in the Materials and Methods section and passaged twice (2P-CF) at days 5-7 (P1) and 9-11 (P2), and exposed to starvation at day 15 after isolation. B Starvation in the presence of inflammatory conditioned medium (inf-CM) prevents PC formation on 2P-CF, as observed in starvation medium (PBS, 0.
Microscopy and imaging Histological slides were scanned using Nanozoomer 2. 0 HT scanner with a 40× objective and visualized with NDP. view2 viewing software (Hamamatsu). Fluorescent images were acquired on a brightfield microscope (Leica) using Metamorph software, inverted Confocal SP5 (Leica) using the Leica LAS AF software, or an Andor Dragonfly Spinning Disk Confocal microscope employing Imaris software. Quantification of PC and vimentin positive fibroblasts was done on at least five random fields (about 1 mm 2 each) per sample using Imaris and ImageJ software. Images were processed with ImageJ. Images were assembled and adjusted with Adobe Photoshop/ Illustrator. Counting of PC was performed using z-stack acquisition. Co-staining with cell markers was confirmed on distinct layers. In analyses of human biopsies, PC numbers were calculated either per mm 2 or per Vimentin-stained area using an arbitrary unit (AU corresponding to 10,000 square pixel) of vimentin positive cells.
Proteomics data processing Proteomics raw data were processed using a free computational platform, MaxQuant version 1. 6. 6. 0 (Max-Planck Institute for Biochemistry, Martinsried, Germany). Peak lists, generated in MaxQuant, were searched using Andromeda peptide search engine against the UniProt database (released 2019_11, taxonomy Mus Musculus, 21,990 entries) supplemented with frequently observed contaminants and containing forward and reverse sequences. Carbamidomethylation of cysteines (C) was defined as fixed modification, while oxidation of methionines (M) and deamidation of asparagines (N) and glutamines (Q) were set as variable modifications. Mass tolerances were set by default to 0. 07 Da in the first search and 0. 006 Da in the main search, instead TOF MS/MS match tolerance was set to 0.
Bioinformatics analyses Quantification of the transcript reads was performed using the Kallisto program  (Bray et al, 2016) , with default settings and the transcriptome from the NCBI mm10/GRCm38 genome as reference. Raw data are available in the NCBI's Gene Expression Omnibus (GEO) database (GEO; https://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE207877). The next steps were performed in the R environment 3. 5. 1. Genes with at least three nonzero samples and a mean of three reads by non-null sample were kept for the downstream analysis steps. To perform the data normalization and the differential expression analysis (DEA) of CM-DSS-CF versus CM-CTL-CF paired samples, we ran standard steps of DESeq2 on the filtered data. We applied a cutoff threshold of 0. 01 to the adjusted P-values and a cutoff threshold of 2 to the absolute fold changes, resulting in a list of 782 significantly differentially expressed genes (479 upregulated and 303 downregulated in DSS).
To study functions involved in the inflamed colon fibroblasts we searched for enrichment with the 50hallmark gene sets from the MSigDB v7. 5. 1 Molecular Signatures Database (https://www. gsea-msigdb. org/gsea/msigdb) by computing their metagene score. For a given sample the metagene score is defined by the mean expression of the gene set. The corresponding mouse orthologs of the human genes have been used. We compared the metagene scores of inf-CM and co-CM-treated fibroblast samples using a two-sided t-test. Differentially expressed metagenes were defined as significant by a P-value < 0. 05.
Acknowledgments We are grateful to the excellent platforms in Montpellier, i. e. , Montpellier Ressources Imagerie platform, the histology and animal experimentation platforms RHEM and RAM, as well as the IGMM mouse facility ZEFI. Many thanks to  Thierry Gostan  and the platform SERANAD for help with the statistical analysis. This work was supported by  INCA  (project  2014-215 , to MH, CJ),  Plan Cancer et Cancer Inserm-ITMO Aviesan 2021  (to MH, CJ,  EM ),  ANR  ( AAPG2021 , CILCOL to MH, CJ, EM), and by the  SFNGE (Grant FARE ) to MS and TE. The authors are thankful to  Professor Kollias BSRC Fleming Greece , for providing the Col6-Cre mice,  Professors Goldstein  and  Yoder  for making available Kif3aflx/flx and IFT88flx/flx strains, respectively, and to  Dr. Stephanie Gofflot ,  Biobank CHU Liege  for providing clinical information.
Artery Segmentation and Frame In-Plane Alignment A Mask R-CNN with inception backbone was used to segment the femoral artery. Trained weights on the COCO dataset were used for initialization. Since there is only one femoral artery in the considered frames, the network was constrained to keep only the mask with the highest detection probability. US sequences were manually annotated using LabelMe  [16]  to feed the Mask R-CNN. A total of 12 out of the 111 sequences from 6 volunteers were manually segmented. The database was split into 6 (4 volunteers, 2073 frames), 3 (1 volunteer, 702 frames), and 3 (1 volunteer, 1004 frames) sequences for training, validation, and testing, respectively. The network was optimized with the TensorFlow's Object Detection API using gradient descent with a fixed learning rate of 1x10 -4 and a momentum coefficient of 0.
9 to speed up the convergence. Random horizontal flip was used for data augmentation since similar information is present between the frame of the leg and the flipped frame from the other leg. The batch size was set to 1 due to memory limitations. An input size of 688×688 was used to preserve the initial pixel size of 0. 056mm to keep the maximum of image information. Dice scores were calculated between the manual segmentation and the output masks of the network to evaluate the resulting model. The entire database was segmented using the final trained network. To handle with false negative cases, we performed spatial interpolation of the output masks through the sequences. The position and the shape of masks were linearly interpolated to generate masks on the frames where the network did not detect the artery.
Out Of Plane Motion Estimation Because neural networks need a fixed input size, an input placeholder of 2 channels was created with enlarged height and width so it could contain an aligned pair of frames. The first frame was centered in the placeholder's first channel. Then, the second frame was included by aligning both barycenters. The contrast of all input frames was improved using contrast limited adaptive histogram equalization (CLAHE). The network predicts the relative longitudinal motion (in mm) between the input registered pair of frames. The CNN contains 3 convolutional layers, 2 max-pooling layers, and 2 fully connected layers. Regarding the limited size of our database and the single out-of-plane translation to estimate, 3 convolutional layers were sufficient to prevent overfitting and reduce the complexity of the extracted features.
The L1 loss function was used as the criterion to be minimized during training. 5-fold cross-validation was used for training and validation. This validation process has been shown to be statically relevant for such small datasets  [17]. For each fold, the whole 111 tracked US sequences of the 18 volunteers were used and split into 10 volunteers for training, 4 volunteers for validation, and 4 volunteers for testing. Each set contained data acquired by both operators. Between 13087 and 15687, 4323 and 4916, and 4058 and 5789 consecutives pairs of original frames were obtained for each fold for training, validation, and testing respectively. Data augmentation was eventually applied by horizontally mirroring each frame during the training phase. The network was implemented by using the publicly available Pytorch library.
The final drift error refers to the absolute difference between the position of the last frame of the volume reconstructed with the position provided by the tracker (i. e. , ground truth) compared to the position calculated with the network outputs. Only sequences longer than 10 cm were considered to compute the drifts errors, representing 70% of all our sequences. Our method was compared to a naïve method and a 1-axis CNN. The naïve method, also called the linear motion method, consisted of computing the average out-of-plane translation on the training dataset and applying it to each frame of a sequence. The 1-axis CNN corresponds to the same network used in our method but without artery alignment, by simply taking each pair of consecutives frames as input.
Femoral Artery Segmentation The network was trained during 30 epochs. On the testing database of 1004 frames, our network reached a false negative rate of 7% and a mean dice score of 0. 90 computed on the true positive cases. Only two frames presented a false positive detection, where the network segmented the genicular artery bifurcation instead of the femoral artery. Example of artery segmentation results are shown in Fig. 2. The inference time was relatively low with an average of 0. 85s per frame on a standard CPU.
Discussion A complete method for generating stretched reconstruction of the femoral artery is presented. The generated volumes provide rich diagnosis information. Planes can easily be extracted to give a direct visualization of the artery allowing fast artery diameter measurement and lesion sizing. The proposed approach was compared to a naïve method of applying a single inter-frame distance calculated on the training database. It would have been of interest to adapt this method by measuring the total length of each sequence at the time of acquisition and using this length to estimate an interframe distance for each sequence. While this method would reduce the final drift error, it would suffer from potential inaccuracies when measuring the total length and would remain limited in case of sequences with speed variations.
Our method was not compared to 6 degrees of freedom CNN  [9]  since it disregards orientation and approximates in-plane motion by centering the artery. Thus, the reconstructed volume contains anatomical deformations beside the artery, whereas the 6 degrees of freedom CNN is designed to build a full US volume. The impact of not considering orientation in our reconstruction method was estimated by extracting orientation through all sequence acquisitions with the optical tracker. The three Euler angles have been computed and the absolute difference between 2 successive frames in all the sequences in our database was on average 0. 07, 0. 07, 0. 08 degrees for roll,  pitch, and yaw, respectively. The relatively small rotational motion in our sequences demonstrates the low tortuosity of the femoral artery and justifies ignoring orientations in our method.
The results on femoral artery segmentation are satisfying considering the small size of our training database (2073 frames). To minimize false negatives, some ground truth segmentations were made on frames where the femoral artery was not distinctly visible. The segmentation gives a clear view of the artery and allows direct quantitative measurements of diameters. Many hyperparameters had to be optimized to design the out-of-plane motion CNN. Tests were performed on the number of convolution layers, the size of fully connected layers, or the type of optimizer to use. Experiments were also carried out by including pairs of non-consecutives frames, but it degraded the performance of the network. The network was also evaluated using the L2 loss to minimize frame to frame large errors but without success.
Extra channels have been added to the input to add the optical flow field using Farnebäck estimation, but it didn't improve the network performance since it only focuses on out-of-plane translation. The consistency loss function was not tested for the same reason. 80% of the test database showed a final drift of less than 15% with our reconstruction method, which is compatible with lesion lengths computation in PAD procedures. The high maximum final drift errors came from sequences for which the operator scanned the artery with more abrupt gestures. The final drift error is particularly meaningful for our application since it measures the length error of the reconstructed artery and evaluates the cumulated error. Experiments were performed on input frame pixel size concluding that a resolution of 0.
We present the Zero Resource Speech Challenge 2021, which asks participants to learn a language model directly from audio, without any text or labels. The challenge is based on the Librilight dataset, which provides up to 60k hours of audio from English audio books without any associated text. We provide a pipeline baseline system consisting on an encoder based on contrastive predictive coding (CPC), a quantizer (k-means) and a standard language model (BERT or LSTM). The metrics evaluate the learned representations at the acoustic (ABX discrimination), lexical (spot-the-word), syntactic (acceptability judgment) and semantic levels (similarity judgment). We present an overview of the eight submitted systems from four groups and discuss the main results.
Lexical Semantics: the sSIMI similarity metrics. Here, as in  [9] , the task is to compute the similarity of the representation of pairs of words and compare it to human similarity judgements. As for the ABX task, participants provide embeddings for input tokens as well as a distance to compute similarity. Here, we provide by default the cosine distance computed over pooled embeddings (with mean, max or min pooling). We used a set of 13 existing semantic similarity and relatedness tests: WordSim-353  [10] , WordSim-353-SIM  [11] , mc-30  [12] , rg-65  [13] , Rare-Word (or rw)  [14] , simLex999  [15] , simverb-3500  [16] , verb-143  [17]  , YP-130  [10]  and the relatedness-based datasets include MEN  [18] , Wordsim-353-REL  [11] , mturk-287  [19] , and mturk-771  [20]. All scores were normalised on a 0-10 scale, and pairs within a same dataset containing the same words in different order were averaged.
Pairs containing a word absent from LibriSpeech train set  [2]  were discarded. We selected as development set the mturk-771 dataset and the other 12 datasets were used as test sets, making sure that no pair from the development set was present in any of the test sets. We then created two subsets of audio files, one synthetic (using the Google API), one natural obtained by retrieving the audio extracts from LibriSpeech corresponding to each word, following the process presented in  [9]. In this subset, each word can appear in multiple tokens, providing phonetic diversity; duplicated scores are averaged in the analysis step. The natural subset is smaller than its synthesised counterpart, as we had to discard pairs from the test and dev sets which were not present in the LibriSpeech test and dev sets respectively.