dataesr/scientific-paragraphs-categorization huggingface dataset:
General primer sets We used the PR 2 database  (Guillou et al. 2013)  which currently contains about 180,000 18S rRNA sequences with detailed taxonomic annotations to test all primer sets from the pr2-primers database. We also determined, using a set of more than 8,500 sequences representative of diverse archaeal and bacterial groups, whether these primers amplified bacteria or archaea. We only used long sequences (see Material and Methods) and allowed for a maximum of 2 mismatches on both forward and reverse primers, i. e. a maximum of 4 mismatches. For general primers, amplification success varied from 32 to more than 97% (Table  S3 , Fig. 2  and S1 ). In general, the reverse primer had a tendency to have more mismatches than the forward primer (Table  S3 ). Primer sets targeting regions other than V4 or V9 did not perform as well in general (Fig.
2 ). The post sequencing analysis strategy in this case would be to only use one of the reads (R1 is in general less noisy) without trying to assemble R1 and R2  (Lambert et al. 2019)  or to assemble the non-overlapping R1 and R2 reads with an intercalated N base  (Needham and Fuhrman 2016). Another important consideration is whether amplification is similar across the whole eukaryotic tax-pr2-primers database -p. 8 onomic range. Taking as an example the most frequently used primer set targeting V4 (# 8, Fig. 3A ) and looking at the amplification efficiency at the supergroup level, a significant fraction of Excavata and to a smaller extent of Rhizaria present at least 5 mismatches to this primer set (Fig. 3A  top-left). Amplification is even more unlikely for sequences presenting mismatches with the forward primer because the mismatches are located at the 3' end of the primer (Fig.
Specific primer sets In order to access a deeper diversity within a given taxonomic group primer sets have been developed with specific targets (Tables  S1  and S2 ). Target levels are most often at the division (e. g. Haptophyta) and class levels (e. g. Chrysophyceae), although some sets are targeting supergroups (e. g. SAR pr2-primers database -p. 9 # 84). Some primer sets have even more specific targets. One example is primer # 65 targeting  Cercozoa (S616F Cerco and S947R Cerco, Fiore-Donno et al. 2018 ) that contains at least 5 mismatches to all other divisions (Fig. S5 ) and amplifies all cercozoan groups. Primer # 38 targeting Chlorophyta (ChloroF and ChloroR,  Moro et al. 2009 ) contains at least 5 mismatches to all other divisions (Fig. S5 ). However, it is does not amplify all Chlorophyta as it misses picoplanktonic green algae such as Mamiellophyceae or Chloropicophyceae (Fig.
R Shiny application We have developed a website based on an R Shiny application (https://app. pr2-primers. org) that allows users to visualize and download the pr2-primers database, explore at different taxonomic levels the results of in silico amplification against the PR 2 and Silva databases for the primer sets from the pr2-primers database and test their own primer sets. The application is composed of 7 panels. The first panel (Fig. 4A ) provides information on the database as well as a link to report issues or new primers. The second and third panels (Fig. 4B ) provide an interface to the primer and primer set tables, respectively, with the options of downloading the tables and revealing/hiding specific columns. The fourth and fifth panels are used to display the results of pre-computed in silico amplification of primer sets from the database.
The fourth panel (Fig. 4C ) shows a synthesis of the results (similar to Fig. 2 ) for all primer sets. The fifth panel (Fig. 5A ) is a tool to explore amplification properties of a given primer set within a taxonomic level from kingdom to class levels. The right-hand section of this panel shows general amplification characteristics, the location of the mismatches, the number of mismatches for each group and the distribution of the amplicon sizes. Finally, the sixth and seventh panels (Fig. 5B ) allow users to run an in silico amplification with their own primers/probes (panel 6) and primer sets (panel 7) against PR 2 and Silva seed databases. Users can fix the maximum number of mismatches (up to 2 for each primer). For the sake of speed, only the number of mismatches is provided, not their position.
Conclusion The combination of the pr2-primers database with the PR 2 sequence database provides a very useful resource for protist metabarcoding. It will help researchers to select the most suitable primer pairs for both broadly-targeted surveys and studies focusing on target taxonomic groups, and to test and validate in silico novel primers. We emphasize that primer pairs must also be tested on reference culture material and natural samples, as actual amplification may differ from in silico results. Hopefully this database will grow with time as novel primer pairs are developed and tested on samples from a range of environments. This will contribute to better design and comparability of microbiome analyses, inventories of protist diversity across environments, and increase our understanding of this functionally diverse and important group of organisms.
S2 ) for the 18S rRNA gene against the PR 2 reference database (version 4. 12. 0). See Fig. 2  for legend. Figure  S3  Evaluation of general primer sets (Table  S2 ) targeting the V4 region of the 18S rRNA gene against bacterial 16S rRNA sequences from the Silva seed reference database (version 132). Legend as in Figure  2. gene region general specific    S2 ) targeting the V4 (top) and V9 (bottom) regions of the 18S rRNA gene against the PR 2 reference database (version 4. 12. 0). Left panel. Percentage of reference sequences with at most 2 mismatches to either forward and reverse primer or to both primers, corresponding to the percentage of sequences amplified by the primer set. Central panel. Number of mismatches for each primer set. Right panel. Amplicon sizes targeted by different primer pairs.
List of Figures 37F 1 37F-41F 2 V1-V2 1 1 V1-V3 1 V2 3 V2-V3 1 3 V3 1 V3-V4 2 V4 32 15 V4-V5 1 V5 3 V5-V7 1 V5-V9 2 V6 1 V6-V8 1 V7 2 V7-V8 1 V7-V9 1 1 V8-V9 2 V9 68 37F pr2-primers database -p. 22.
Figure1Position of the amplified region when using different primer sets listed in the pr2-primers database along the 18S RNA gene relative to the sequence of the yeast Saccharomyces cerevisiae (FU970071). The labels correspond to the primer set id, the 18S region amplified, its identification name and the specific group it eventually targets. Bar shading indicates whether the primer is general (black) or specific (grey) of a taxonomic group.
Figure1: Position of the amplified region when using different primer sets listed in the pr2-primers database along the 18S RNA gene relative to the sequence of the yeast Saccharomyces cerevisiae (FU970071). The labels correspond to the primer set id, the 18S region amplified, its identification name and the specific group it eventually targets. Bar shading indicates whether the primer is general (black) or specific (grey) of a taxonomic group.
Within the COVID-19 neg ARDS pos group, ARDS etiologies were bacterial pneumonia (n = 9), anti-synthetase syndrome (n = 1), and unknown (n = 2) (Table  S1 ). For 21 patients, a second blood sample obtained on day 7 after enrollment was studied (n = 7 for COVID-19 neg ARDS pos , n = 8 for COVID-19 pos ARDS pos , and n = 6 for COVID-19 pos ARDS neg ). Additionally, a validation cohort (cohort 2) was set up with 16 patients with demographic data detailed in Table  S1  and Table S2.
Limitations of Study Besides the low number of included patients, our study has some limitations. By focusing on severe patients with and without ARDS, we cannot make conclusions about phenotypic changes in mild and moderate diseases. The analysis would also benefit from comparison with other virusassociated ARDS. We thus analyzed a published dataset of flu-like illness and COVID patients, analyzed by mass cytometry. 21 Interestingly, by using CellCnn, we were able to define a filter that accurately discriminate flu-like illness from COVID samples, suggesting immune differences between both diseases (Fig. S4 ). Moreover, since the mass cytometry was conducted on PBMCs, J o u r n a l P r e -p r o o f we lack information on the neutrophil lineage, which appears affected in COVID-19 disease. 21 It would also be interesting to link these data with in situ data from lung tissue samples and bronchoalveolar lavages.
Severity criteria Maximal O 2 (L/min), median (IQR) 10 (7. 5-15)  samples were drawn at D0 and D7. PBMC were isolated from whole blood using ficoll before cryopreservation. All patients provided written informed consent. The following data were recorded: gender, age, preexisting chronic kidney disease and acute kidney failure during the ICU stay,  61  preexisting chronic heart failure,  62  Body Mass Index (BMI), SAPS II at admission,  63  duration of mechanical ventilation, length of hospital stay, and outcome (alive or dead) on day 7, day 30 and day 90. The occurrence of nosocomial infection, defined following CDC criteria as previously described,  64  was also recorded during hospital stay. For each patient, a clinical score was built to summarize the occurrence of adverse clinical events frequently encountered during hospitalization.
Mass Cytometry Preprocessing J o u r n a l P r e -p r o o f After acquisition, intrafile signal drift was normalized and. fcs files were obtained using CyTOF software. To diminish batch effects, all files were normalized on EQ Beads (Fluidigm Sciences) using the premessa R package (https://github. com/ParkerICI/premessa). Files were then uploaded to the Cytobank cloud-based platform (Cytobank, Inc. ). Data were first arcsinh-transformed using a cofactor of 5. For all files, live single cells were selected by applying a gate on DNA1 vs. DNA2 followed by a gate on DNA1 vs. Cisplatin, then beads were removed by applying a gate on the beads channel (Ce140Di) vs. DNA. 1 Normalized, transformed and gated values were exported as FCS files.
CellCnn analysis Identification of a Covid-19-specific cell-identity signature was carried out using the CellCnn algorithm,  35  implemented in Pytorch in the ScaiVision platform (version 0. 3. 6, © Scailyte AG). Briefly, this is a supervised machine learning algorithm that trains a convolutional neural network with a single layer to predict sample-level labels using single-cell data as inputs. Data from each CyTOF panel was analyzed separately, in each case using all measured protein markers to train a.
r n a l P r e -p r o o f EXPERIMENTAL MODEL AND SUBJECT DETAILS Patients This study was performed in the infectious diseases department and intensive care unit (ICU) at Rennes University Hospital. The study design was approved by our ethic committee (CHU Rennes, n°35RC20_9795_HARMONICOV, ClinicalTrials. gov Identifier: NCT04373200) and informed consent was obtained from patients in accordance with the Declaration of Helsinki. Patients with malignancy, HIV-infected patients, and patients with preexisting immune disorders or receiving immunosuppressive agents were excluded. The presence of SARS-CoV-2 in respiratory specimens (nasal and pharyngeal swabs or sputum) was detected by real-time reverse transcription polymerase chain reaction (RT-PCR) methods (TaqPath COVID-19, ThermoFisher). Peripheral blood was collected in tubes containing lithium heparin from COVID-19 neg ARDS pos , COVID-19 pos ARDS pos , and COVID-19 pos ARDS neg patients.
Cohort 1 : 1 r n a l P r e -p r o o f EXPERIMENTAL MODEL AND SUBJECT DETAILS Patients This study was performed in the infectious diseases department and intensive care unit (ICU) at Rennes University Hospital. The study design was approved by our ethic committee (CHU Rennes, n°35RC20_9795_HARMONICOV, ClinicalTrials. gov Identifier: NCT04373200) and informed consent was obtained from patients in accordance with the Declaration of Helsinki. Patients with malignancy, HIV-infected patients, and patients with preexisting immune disorders or receiving immunosuppressive agents were excluded. The presence of SARS-CoV-2 in respiratory specimens (nasal and pharyngeal swabs or sputum) was detected by real-time reverse transcription polymerase chain reaction (RT-PCR) methods (TaqPath COVID-19, ThermoFisher).
These methods typically use a closed roof outline to describe the roof structure assuming that the interior topology can be determined by the roof outline. The roof face planarity is enforced during the computation of roof topology from the input outline. Another solution is to use general purpose commercial software such as 3ds Max and SketchUp to model roofs. Though the commercial software can construct large variations of realistic roofs, the roof planarity is either ignored (e. g. , 3ds Max) or implicitly enforced by triangulation (SketchUp). We observe that these solutions have limitations in different aspects (see Table  1 ). For example, using only the outline for roof structure specification is simple but not sufficient for roof modeling, since different roofs can have the same outline.
In summary, our main contributions are: • A novel roof modeling method including a simple roof graph representation that encodes roof topology and a new planarity metric that can be used to enforce geometric constraints for generating 3D polygonal roof meshes. • An optimization-based framework that is complementary to learning algorithms and user input for automatic roof synthesis and interactive editing. • We created a dataset consisting of 2539 roof meshes paired with images without topological or geometric errors (see Fig. S17 ) using our method, which can be helpful for different visual computing tasks. Code and data are available. 1 Fig. 5. We created a dataset of 3D planar roofs paired with the corresponding aerial images. Here we show 150 example roofs and highlight the topology. The paired images are shown in Fig.
Roof Reconstruction Urban reconstruction aims at automatically generating 3D models from real physical measurements, such as multi-view images or point clouds  [Demir et al. 2015; Musialski et al. 2013]. We focus our review on recent methods that have roof reconstruction as a major component. One category of algorithms uses optimization. For example, Zhou and Neumann published a series of papers to reconstruct coarse building models including roofs from LiDAR point clouds  [2008; 2010; 2011]. Lin et al. [2013]  propose to find a combination of planar primitives that best explains the input Li-DAR data for roof reconstruction. Dehbi et al. [2021]  propose an active sampling strategy for RANSAC to fit plane approximations to input point clouds. Nan and Wonka [2017]  and  Kelly et al. [2017]  employ integer programming to reconstruct coarse planar building models.
Each conditional density models a single element in the sequence conditioned on all previous elements. Language models are auto-regressive in nature  [Dai et al. 2019; Radford et al. 2019]. Some works also model images as sequences, e. g. ,  PixelRNN [Van Oord et al. 2016 ] and its follow-up works PixelCNN [  Van den Oord et al. 2016] , PixelCNN++  [Salimans et al. 2017] , and PixelSNAIL  [Chen et al. 2018]. With the rise of transformers, autoregressive models have been increasingly applied to 3D data, including polygonal meshes  [Nash et al. 2020] , floor plans  [Para et al. 2020] , and scenes  [Wang et al. 2020 ]. We argue that the discrete nature of ARs is a unique advantage for modeling 3D data that does not consist of smoothly varying models, but needs a latent representation that can handle discontinuities.
We quantitatively measure the topological errors ("#err") of the constructed roofs and report them in Table  3. Specifically, we measure how many non-planar faces in each roof constructed in 3ds Max. In SketchUp, the expert hid some edges of the constructed roofs as shown in Fig. 24. We can then use the "unwanted face complexity" as a measure to evaluate the topological errors in SketchUp. In summary, compared to the commercial software for roof modeling, our method is more efficient and simpler for novice users. The roofs constructed using our method have a simpler topology while the roof planarity and the visual consistency to the input image are enforced.
Image-Building Paired Dataset image polygon mesh point cloud We created an image-building paired dataset using our roof optimization method, where a complete building is constructed by adding facade planes along the roof outline and a base plane at the bottom. Specifically, we created a dataset consisting of 2539 buildings paired with the input aerial image and face labels (including roof faces, facade faces, and base face). The annotations are cleaned automatically by merging close-by vertices, removing duplicate or redundant edges/vertices, and our method is then used for roof reconstruction. See Fig. S17  for some example buildings and Fig. 25  for a summary of the roof complexity including the number of roof vertices and roof faces in this dataset. We believe this dataset can be helpful for different visual computing tasks.
Specifically, this is a mesh-image paired dataset, which can be used for learning-based roof mesh detection. We also assign each polygon face in the building mesh a label from the set of roof face, body face, or base face. We can sample from the polygon mesh to obtain a point cloud as well (see inset figure). This dataset also contains roofs with a larger range of complexity. For example, in the following we discuss how to use this dataset for roof synthesis from scratch. 249:12 • Jing Ren, Biao Zhang, Bojian Wu, Jianqiang Huang, Lubin Fan, Maks Ovsjanikov, and Peter Wonka.
Application 1: Roof Synthesis from Scratch As discussed in Sec. B, our roof modeling formulation can simplify the roof synthesis problem. Specifically, we propose learning-based techniques for roof topology synthesis, and then use our roof optimization method to enforce geometric planarity constraints. We use the constructed dataset in Sec. 6. 2 to train models on roof graph generation, including a transformer for roof outline generation and a graph neural network for face adjacency prediction. See Fig. S7  for some example roof outlines generated by our transformer. Our graph neural network can predict the probability of adjacency for face pairs, from which we can extract either a single dual graph with highest probability (see Fig. S10 ) or multiple valid dual graphs for roof construction as shown in 27.
Implementation & Runtime We implemented the roof optimization methods (including spectral embedding and planarity optimizations) in MATLAB and used the build-in function "fmincon" with Quasi-Newton solver for optimization. We designed a web-based GUI with two modes for collecting user inputs of primal-dual roof graph specification for the application of roof reconstruction from images. The roof synthesis application is implemented using Pytorch  [Paszke et al. 2019]. In Table  4  and Table  5 , we report the roof complexity and computation time of our method for each roof in Fig. 20  and Fig. 31  respectively. We can see that our algorithms, both 2D spectral embedding and planarity optimization, are efficient and robust w. r. t. various roof outlines with different complexity.
CONCLUSION, LIMITATION & FUTURE WORK We proposed an optimization-based roof construction method that first designs a primal or dual roof graph as input and then optimizes the geometry to output a planar 3D polygonal roof. Our formulation is flexible and can be adapted easily to different settings such as incorporating user-specified regularizers. Our method has two  practical applications, interactive roof editing and roof synthesis from scratch. Our method of roof reconstruction is more expressive than the straight-skeleton based methods, and is much easier for novice users to use than commercial software. We also use our method to construct a image-building paired dataset with 2539 roof meshes, that can be helpful for different visual computing tasks. Although our method can be used to model roofs with different styles, including buildings with inner courtyards or vertical facades inside the roof, and buildings with outline edges in different height as shown in Fig.
Further, the interactive roof modeling is still fairly slow so that it is hard to scale up the dataset construction by another order of magnitude. For practical reasons, it might be very important to investigate efficient combinations of automatic and interactive reconstruction that would be time-saving without increasing the error rate compared to a human only baseline. Finally, we did not touch on automatic reconstruction in this paper. In future work, we would like to investigate transformers using images for cross attention following to the work of  Dosovitskiy et al. [2021] , and investigate how to predict roof graphs from images directly by using the image-mesh dataset we constructed. It would also be interesting to study practical constraints for roof fabricability using our optimization-based formulation, such as incorporating slope requirements of roof faces during the construction, which can be addressed by either hard or soft constraints.
S6 ). A similar embedding strategy can be found in PolyGen  [Nash et al. 2020 ]. Transformer blocks. The transformer is composed of a series of transformer blocks. Each block is as follows, ℎ (𝑙) ← ℎ (𝑙-1) + MultiheadAttention ℎ (𝑙-1) , (S3a) ℎ (𝑙) ← LayerNorm ℎ (𝑙) , (S3b) ℎ (𝑙) ← ℎ (𝑙) + MLP ℎ (𝑙) , (S3c) ℎ (𝑙) ← LayerNorm ℎ (𝑙) , (S3d) where ℎ () represents the hidden representation of   in -th block, MultiheadAttention(•) is a (masked) multihead self-attention layer  [Vaswani et al. 2017 ] and MLP(•) is a position-wise 2-layer fully-connected network. Architectures. We build the transformer with 6 blocks. We use 384 as the embedding dimension, 12 heads in the self-attention modules and 1536 as the hidden dimension in the MLPs. To make the model auto-regressive, we mask the sequences and allow the attention modules only attend previous tokens  <.
Our dataset contains 2300 training samples and 239 testing samples. We optionally skip samples with only four vertices to avoid simple roof outlines. After that, the numbers of training and testing samples are reduced to 2105 and 210, respectively. Learning Results. We train this model for 100 epochs. The optimizer is Adm [Kingma and Ba 2014] with a fixed learning rate 2 -4. We evaluate the model using the negative log-likelihood (NLL). As a result, we achieve 1. 051 NLL on the training dataset, and 0. 964 NLL on the test dataset. ).
ACKNOWLEDGMENTS The authors thank the anonymous reviewers for their valuable comments. Parts of this work were supported by the  KAUST OSR  Award No. CRG-2017-3426 , the  ERC Starting  Grant No. 758800  (EXPROTEA), the  ANR AI Chair AIGRETTE , and  Alibaba Innovative Research (AIR) Program. We would like to thank  Guangfan Pan  and  Jiacheng Ren  for helping modeling roofs in 3ds Max and SketchUp,  Jialin Zhu  for helping designing the web-based roof annotation UIs,  Jianhua Guo  and  Tom Kelly  for helping with the comparison to the weighted straight skeleton. We thank  Muxingzi Li  for helping editing the supplementary video. We also thank  Chuyi Qiu ,  Tianyu He , and  Ran Yi  for their valuable suggestions and comments.
1 4-3-3 proteins recognize protein partners phosphorylated at serine or threonine in certain sequence motifs in all eukaryotic organisms. The seven human 14-3-3 isoforms, individually named β, γ, ε, ζ, η, σ, and τ (beta, gamma, epsilon, zeta, eta, sigma, and tau)  1  , are distinct gene encoded paralogs that are highly similar in sequence and in their phosphopeptiderecognition mode, yet display different expression patterns across tissues  2, 3. 14-3-3 proteins are highly abundant in most human tissues, where several 14-3-3 isoforms are systematically found among the top 1% of the ~20,000 human gene-encoded proteins  3. For instance, according to the Protein Abundance Database, PAXdb  3  , the cumulated seven 14-3-3 isoforms are within the five most abundant protein species in platelets. 14-3-3 proteins have a highly conserved dimeric all-helical structure  1, 4.