dataesr/scientific-paragraphs-categorization huggingface dataset:
Application to SL9 and comparison with a polyhedral method In this section, we apply our SSDEM to study the most famous tidal disruption event, the encounter of comet Shoemaker-Levy 9 (SL9) with Jupiter in 1992. This event has provided valuable information to benchmark the physical properties of small bodies. Asphaug & Benz (1994 , 1996)  performed the numerical modeling of this disruptive encounter using two kinds of approaches and progenitor models: a non-rotating monolithic sphere modeled with their SPH code with tensile strength  (Benz & Asphaug 1994) , and a spherical rubble-pile aggregate consisting of frictionless equal-sized spheres modeled with an N-body code with soft elastic collisions. A better match was found with the rubblepile SL9 model and the estimated bulk density for SL9 is about 0.
It is nevertheless possible that the use of spherical shapes, even considering high friction, may not capture the influence of irregular shapes in some particular conditions or contexts. Thus, other studies using other implementations with irregular shapes would be useful to check against when such prescriptions are necessary. For instance, in a future study, we would use our SSDEM code and replace spherical particles with rigid aggregates made of such particles in our SSDEM code, as done in  Richardson et al. (2009)  and  Thuillet et al. (2019) , to investigate under which conditions, and in which contexts, modeling explicitly irregular shapes is required. We also found that the particle configuration in a rubble pile has great influence on the encounter outcomes. With the same encounter conditions, the RCP model generally leads to more severe changes due to tidal forces than the HCP model.
Acknowledgements. We thank  Kevin Walsh  and  Derek C. Richardson  for inspiration and suggestions on exploring the differences between HSDEM and SS-DEM for simulating close encounters. We also thank the anonymous referee for the constructive reviews. Y. Z. acknowledges funding from the  Université Côte d'Azur  " Individual grants for young researchers" program  of IDEX JEDI. Y. Z. and P. M. acknowledges support from  CNES  and from the  Acad. 2 and 3 of  Univ. Côte d'Azur IDEX JEDI. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 870377  (project  NEO-MAPP ). Simulations were performed on the LI-CALLO cluster hosted at the  Observatoire de la Côte d'Azur  and on the yorp cluster administered by the  Department of Astronomy  and the Deepthought2 HPC cluster administered by the  Division of Informational Technology at the University of Maryland.
2012; Dyrka et al. 2014). Several bacterial proteins have received a RHIM amyloid motif annotation in the Pfam or InterPro databases  (Rebsamen et al. 2009; Sun et al. 2002). We have analyzed the corresponding protein sequences and found that the region annotated as RHIM occurs in a similar genomic context as fungal amyloid signaling motifs. The motif occurs at the N-terminus of a NLR-like protein and at the C-terminus of a putative effector protein encoded by an adjacent gene. Here, we systematically explore in a genome mining approach the occurrence of putative amyloid signaling sequences in bacterial and archaeal genomes and identify ten families of bacterial amyloid signaling sequences (named here BASS1 to 10). The family designated as BASS3 corresponds to the RHIM-like sequence. We find these motifs specifically in multicellular bacteria in particular in Actinobacteria and Cyanobacteria.
RHIM-like motifs in Bacteria In the Pfam database, the majority of the sequences with a RHIM annotation are from Metazoan or Metazoan viruses  (El-Gebali et al. 2018; Kajava et al. 2014; Kleino et al. 2017; Li et al. 2012; Pham et al. 2019). A few hits (24/286) however occur in Bacteria. We examined these bacterial RHIM-annotated proteins and found that ten of them are homologous proteins with the RHIM annotation occurring C-terminally, downstream of ~100 amino acid long predicted α-helical domain that we termed Bell (for bacterial domain analogous to Hell, based on shared features with the fungal HeLo-related domains, see below). In the actinobacterium strain Saccharothrix sp. ALI-22-I, the gene adjacent to the gene encoding the Bell/RHIM protein (ONI86675. 1) encodes a protein with NLR architecture (NB-ARC domain and TPR repeats) (ONI86674.
Prediction of amyloid signaling sequence motifs in bacterial genomes Amyloid signaling motifs in fungi occur in adjacent gene pairs  (Daskalov et al. 2012). Based on the observed Bell-RHIM/NLR-like gene pairs (Fig. 1A ), we hypothesized that an amyloid signaling mechanism akin to the one described in filamentous fungi might also operate in some bacterial species. To identify potential amyloid signaling motifs in bacteria we extended a genome mining strategy that we have previously developed to identify amyloid signaling motifs in fungi  (Daskalov et al. 2012 (Daskalov et al. , 2016)). We screened for sequence motifs common to C-termini of Bell-domain proteins and N-termini of NLR-like proteins, which are encoded by adjacent (or closely linked) genes. Specifically, we generated a data set of 1816 non-redundant C-termini of prokaryotic Bell-domain proteins, searched for conserved gapless motifs using MEME and identified 117 motifs.
Methods Homology searches. The N-terminal 102 amino acid-long fragment of protein ONI86675. 1 (A0A1V2QF20_9PSEU) (Yeager et al. 2017) was used as the Bell domain query in Jackhmmer  (Eddy 2011; Potter et al. 2018 ) (20 iterations, 2133 hits) and Psi-blast  (Altschul et al. 1997; Madeira et al. 2019) , (5 iterations, 502 hits) homology searches in UniProtKB  (Consortium 2018 ) and NCBI nr databases  (Coordinators 2018 )(respectively), as of March 2019. In order to extend the coverage, the searches were re-run with the lowest scoring above-the-threshold Jackhmmer hits in Archaea (OPX82726. 1 / A0A1V4VC50_9EURY),  (Nobu et al. 2017) , Chloroflexi (ACL23627. 1 / B8G4Z4_CHLAD) and Proteobacteria (MBN58330. 1 / A0A2E7JCT6_9GAMM)  (Tully et al. 2018) , resulting in 2383, 2553 and 939 hits, respectively.
In all cases, standard website parameters were used. Combined results consisted of 2797 non-redundant sequences including 2354 sequences shorter than 200 amino acids. The length threshold was chosen to filter out Bell homologues forming "all-in-one" NLR architectures. Neighboring NLRs identification. All sequence identifiers in the set were mapped to NCBI accessions using UniProt mapping tool. Accessions of identical sequences in the NCBI nr database were added using the Blast database command tool  (Camacho et al. 2009 ). This resulted in set of 2810 protein accessions from genome-wide studies, restricted to GenBank and RefSeq NP and YP series accessions. An in-house Python (version 3. 5) script (aided by requests (Reitz, K. , n. d. ) and xmltodict (Blech, n. d. ) packages) was used to query NCBI Entrez E-utils in order to fetch almost 23k proteins coded by genes within the +/-5000 bp neighborhood of the genes encoding these Bell homologues.
Among the proteins coded by the Bell-neighboring genes, 730 were matched with Pfam (El-Gebali et al. 2018), NACHT (PF05729)  (Koonin and Aravind 2000)  or NB-ARC (PF00931) (van der Biezen and Jones 1998) profiles. This included 467 sequences (426 unique) where the match bordered a relatively short N-terminus of 10 to 150 amino acids in length (396 unique extensions), which is a typical feature of fungal NLR proteins containing amyloid signaling motifs. Motif extraction. The C-terminal boundaries of the Bell domains were delimited according to the final Profile HMM (pHMM)  (Eddy 2008)  of the original Jackhmmer search using the hmmalign tool of the standalone HMMER distribution (version 3. 2. 1) (Eddy 2011). The C-terminal extensions longer than 10 amino acids were extracted. The redundancy of the set was reduced to 90% using cdhit  (Fu et al.
Motif profile generation. For each motif, a pHMM was trained using hmmbuild from the HMMER package on instances reported by MEME. Then, the pHMMs were used to re-search the Bell Cterminal domains and NLR N-terminal domains, respectively, using hmmsearch with sequence and domain E-values set to 0. 01 and heuristic filters turned off for sensitivity (--max option). The resulting hits were extended each side by 5 amino acids, and then re-aligned to the pHMMs and trimmed of unaligned residues using hmmalign (--trim option). Eventually, the resulting alignments were used to train final pHMMs for the motifs again using hmmbuild with standard options. The entire procedure aimed at generalizing the motifs, especially gapped, which could be truncated or over-specialized by MEME.
Motif characterization. For each member motif in classes BASS1-10, the sequence regions matched with its pHMM were extended each side by 5 amino acids and pairwisely locally aligned using the Waterman-Eggert method  (Huang and Miller 1991; Waterman and Eggert 1987)  implemented in EMBOSS (matcher tool, version 6. 6. 0. 0)  (Rice et al. 2000). The standard parameters were used including the BLOSUM62 matrix (Henikoff and Henikoff 1992) and gap opening/extending penalties 14/4. For each motif class representative, the sequence fragments from the resulting pairwise alignments were combined into multiple sequence alignments using Clustal Omega  (Sievers and Higgins 2018; Sievers et al. 2011)(version 1. 2. 4, standalone)  with the --dealign and --auto switches. The resulting MSAs were trimmed manually and profiles were generated using weblogo3  (Crooks et al.
2004 ) (Weblogo software repository webpage, 2019). In addition, for each motif class and sequence, the pairwise alignments were used to obtain the combined longest fragment matching any member motif. The dealigned fragments were then combined into MSA using Clustal Omega with --full and --full-iter options, curated when necessary (BASS4) and trimmed manually. Amino acid composition of such generated MSAs was then calculated using the quantiprot package  (Konopka et al. 2017) , after removing redundant sequences. The per-class and overall (unweighted) composition was then compared with amino acid composition of non-redundant sequence sets of fungal functional amyloid motifs including Het-s Related Amyloid Motif (HRAM)  (Daskalov et al. 2015a) , Pfam NACHT_sigma (PF17106) and Ses_B (PF17046, aka PP-motif)  (Daskalov et al.
2015a; Dyrka et al. 2014) , beta-solenoid repeat regions extracted from the Protein Data Bank  (Berman et al. 2000)  according to RepeatsDB (only reviewed entries, as of May 2019), experimentally verified amyloid fragments from AmyLoad  (Wozniak and Kotulska 2015)  (as of March 2017), and intrinsically disordered protein regions from DisProt  (Piovesan et al. 2017 ) (as of July 2019, only unambiguous entries). Current SwissProt amino acid statistics were used as a reference.
Phylogenetic distribution. Protein accession lists of genome assemblies listed in the Genome Taxonomy Database (GTDB)  (Parks et al. 2018 ) metadata sheets for Bacteria (113,324 items) and Archaea (1183) were downloaded from NCBI ftp (ftp. ncbi. nlm. nih. gov/genomes/all/; as of April 2019). The accesions were matched with the ABC_tran (PF00005)  (Rosteck et al. 1991) , NACHT, NB-ARC, Beta_propeller (CL0186)  (Murzin 1992) , TPR (CL0020)  (Lamb et al. 1995) , CHAT (PF12770)  (Aravind and Koonin 2002) , PNP_UDP_1 (PF01048)  (Mushegian and Koonin 1994)  and TIR (PF01582)  (Bonnert et al. 1997 )Pfam profiles hit lists downloaded from Pfam ftp (ftp. ebi. ac. uk/pub/databases/Pfam/current_release/; as of February 2019). In addition, distributions of previously found Bell domain homologues and motif pairs involving their C-termini in GTDB-listed genomes were recored.
Phleomycinresistant transformants were selected, grown for 30 h at 26°C and screened for the expression of the transgenes using a fluorescence microscope. Fragments (protein position indicated in brackets) of the following genes (accessions from GenBank or RefSeq) were amplified using specific primers: CAB66307. 1 (110-139), CAB66306. 1 (1-34), WP_037701008. 1 (70-124), WP_037701012. 1 (1-37), ACC79696. 1 (94-126), ACC79697. 1 (1-38),  WP_063130184. 1 (74-128). The PCR products were cloned upstream of the RFP coding sequence in the pOP plasmid using PacI/BglII restriction enzymes or downstream of the GFP in the pAN52. 1 plasmid using NotI/BamHI restriction enzymes. For heterologous expression in E. coli, the following fragments were amplified using specific primers and cloned in pET24a (Novagen) using the NdeI/XhoI restrictions sites: CAB66307.
1 (110-139) or (38-139), WP_037701008. 1 (97-124), ACC79696. 1 (94-126), WP_063130184. 1 (100-128). Since the sequences selected for the BASS3 motif were not included neither in the GenBank nor RefSeq NP or YP series at the time of the genome mining, they were not processed for identifying neighboring NLRs, and hence are not covered in the motif pair list in Table  S2. Nevertheless, the BASS3 motifs can be found in these sequences with the motif pHMMs at default identification threshold (E-value of 0. 01), except for ACC79697. 1 being slightly below (E-value of 0. 1). prion donor strain and after 72h (contact between tested and donor strains was established after 24h of subculture) the initially prion free tested strain was subcultured on fresh medium and monitored for the presence of aggregates using fluorescence microscopy.
2F ). A direct comparison between the OA and normal samples is difficult, however, as the number of OA samples (n = 20) is much larger than normal samples (n = 5). Hence, this dataset may be missing some of the potentially higher variability clusters in normal cartilage. Using these populations, whose unique identities are detailed in later sections, we performed hierarchical clustering of the 20 OA patient and 5 normal samples in our study. Our goal was to identify subsets of patients with unique compositions of these rare populations. Such characterization, common in the cancer field, can be helpful in designing targeted therapeutic strategies tailored to groups of patients with similar molecular underpinnings driving their disease. As expected, all the normal samples clustered together (Fig.
We were able to successfully identify cells that expressed both IL1R1 and TNFRSF1B transcripts in the scRNA sequencing data. These cells represented about ~2% of sequenced cells, validating the frequency we observed by cyTOF. Chondrocytes that expressed both transcripts were sorted in silico, and the differentially expressed genes and pathways were analyzed. The IL1RI/ TNFRII-expressing chondrocytes were found to be highly enriched in pathways related to innate and adaptive immune cells, inflammation, and altered T and B cells signaling in arthritis (Fig. 4F ). These analyses suggest that the IL1RI/TNFRII cells might act to recruit immune cells to the joint space. We, therefore, termed clusters 15   and 20 inflammation-amplifying (Inf-A) chondrocytes. Upon analyzing their signaling status, the Inf-A clusters showed exclusive signaling through pJNK (phosphorylated c-Jun N-terminal kinase) and pSMAD1/5 compared to the rest of the chondrocyte clusters (Fig.
5B ). In agreement with our previous report, CD24 cells decreased with age (fig. S5A ) and were among the least reactive groups to undergo stimulation by the proinflammatory cytokine IL1B (fig. S5B ). Therefore, we termed clusters 17 and 18 inflammation-dampening (Inf-D) I and II cells, respectively. Inf-D II cells had the highest levels of CD24 expression and also had higher levels of Sox9 and CD44, although expression in Inf-D I cells was comparable with normal cells (Fig. 5C ). To further characterize the function of these CD24 + cells, we used the same previously published scRNA sequencing dataset and sorted out CD24 + cells. Consistent with our hypothesis that the CD24 + cells are capable of immune modulation, we observed an enrichment for pathways related to inflammation and immune cell trafficking and cross-talk (Fig.
The single-cell proteomic approach is especially pertinent in robustly identifying rare cell populations that are difficult to discern from RNA sequencing data, where only 1600 cells were studied from all the patients with OA. In contrast, the ability to map 30,000 to 100,000 cells per patient in a 20-patient cohort by the cyTOF method provided us a robust dataset to find and validate statistically significant rare subpopulations. A recent study on rare senescent cell populations in OA cartilage has shown the influence of these small populations in OA pathology  (32). Removal of senescent cells significantly impaired OA progression in a mouse model and modulated end-stage human OA chondrocytes, underscoring the need for further studies on other rare populations that might contribute to OA pathology.
In addition, frequent discrepancies between gene and protein expression have been reported in OA, further signifying the need for complementary proteomic and transcriptomic studies. The ability to measure a large number of cells with high precision allowed us to identify two, rare chondrocyte subpopulations (Inf-A and Inf-D), which constitute only 0. 5 to 1. 5% of all chondrocytes. However, pharmacologically targeting these small populations led to a dampening of inflammatory cytokines at the population level. The Inf-A cells express both the TNFRII and IL1R1 receptors, are consistently expanded in OA compared to normal cartilage, and are characterized by activated JNK1/2 and SMAD1/5 pathways. An analysis of their transcriptomes from the published scRNA sequencing dataset suggests that these cells may function to recruit immune cells.
Data inclusion/exclusion criteria All collected data points were used for assays performed after drug treatment. All datasets were quality controlled, and wells or data points that did not pass quality control metrics were not used. This includes (i) Luminex wells that did not give acceptable standard bead readings, (ii) quantitative polymerase chain reaction (qPCR) wells that did not give suitable C t values for Actin, and (iii) cells analyzed by cyTOF that did not have high SOX9 or CD44 expression. Quality control exclusions were performed before analysis of data. After exclusion of points for these reasons, no additional points were excluded. Replicates All drug treatments were performed in independent technical replicates for each patient (i. e. , cells derived from the same patient were treated three times with drug versus control).
Quality control and data cleaning Normalization over run time was performed using the EU beads using the previously published bead normalized (v0. 3) available here: https://github. com/nolanlab/bead-normalization/releases with the default parameters. Samples were then debarcoded using the single-cell debarcoder available here: https://github. com/nolanlab/ single-cell-debarcoder using the default parameters. Channel values were arcsine transformed and normalized between the two inde-pendent runs using two patients with OA that were loaded in both runs. The tower-independent runs were normalized to each other. Next, we selected for live cells by gating for cisplatin-negative, DNA (Ir195)-positive cells. Last, from live cells, we gated for SOX9/CD44 double-positive cells, which were included in the final analysis.
FlowSOM analysis and tSNE projections Clusters were called using FlowSOM  (10). Analysis was performed using Cytobank's online implementation using the standard settings. Clustering was performed using the cell surface receptors, HIF2A and SOD2 (superoxide dismutase 2); no signaling markers were included. The self-organizing map (SOM) was constructed using the 20 OA and 5 normal samples, and then, the same SOM was applied to the treated samples. tSNE projection was also performed using Cytobank's online platform. All results, including FlowSOM clusters and tSNE coordinates, were exported as text files and manipulated for plotting in Python. We compared the results from our FlowSOM clusters to other clustering algorithms, including X-shift  (24) , and obtained similar numbers of clusters and patterns of expression within each cluster.
Data visualization and analysis Data were visualized using Python and the NumPy (www. numpy. org/), pandas (https://pandas. pydata. org/pandas-docs/stable/), and seaborn (https://seaborn. pydata. org/) packages. Hierarchical clustering of samples or cell populations was performed using the seaborn clustermap function, using a complete-linkage algorithm, also known as the farthest neighbor clustering, in which clusters are decided on the basis of the two most dissimilar points. Complete linkage clustering avoids the chaining phenomenon that can occur with single-linkage methods, where clusters that may be very distant from each other are forced together because of a single element being close. Complete linkage tends to find compact clusters of equal diameters.
Statistical analysis Planned comparisons were performed with the GraphPad Prism software. We used (i) one-way analysis of variance (ANOVA) followed by Tukey's post hoc test to identify specific differences between drug treatment groups or between selected OA patient groups (for treatments, groups were only compared against DMSO controls, not against each other) and (ii) nonparametric, two-tailed Welch's t test for comparisons between only two groups. P values were corrected for multiple hypothesis testing, such that the family-wise error was capped at 0. 05, using the Bonferroni correction method. The exact method and specific P values for significant comparisons are stated in the appropriate results section. For cyTOF plots, although only 9000 cells were visualized on the tSNE plots in the figures, average values and other calculations or statistics were performed with all cells that met the required criteria.
Acknowledgments: We would like to thank  Y. Rosenberg-Hasson  at the  Stanford Human Immune Profiling Center  for help with the Luminex analysis,  E. Migliore  for help with sample acquisition, and  N. Sahu  for helpful feedback on the manuscript. Funding: P. S. and N. B. are supported by  NIH/NIAMS  grants  R01 AR070865  and  R01 AR070864 , F. C. G. is supported by the  NSF   GRFP award. This work was also supported by a gift to the  Department of Orthopedic.
The object of the present study is the integration of a solar cooling and heating system of a residential building (4-flour) located in Blida region in Algeria. The system consists of a set of solar collectors that supply energy to a balloon through an external heat exchanger. The solar and auxiliary tanks are connected by a heat exchanger immersed in the bottom of the auxiliary tank. In the distribution loop, a recirculation can make possible to obtain the domestic hot water set point temperature. A cold production system is directly connected to the solar tank and a regulation system ensures the control of the installation. We have used the TRANSOL software under TRANSYS to calculate the heating and cooling load. Several parameters are then introduced to size and evaluate the performance of our installation.
In the present contribution, we extend the work done by S. Bahria et al. [10]  to the region of Blida, which belongs to the climatic zone B [11], with the specific change of using vacuum collectors integrated in a four levels building instead of flat collectors integrated in a one level construction that has been considered in the paper  [10]. The aim is to achieve higher temperatures as the studied buildings have roofs sloping at 21°. The studied building is part of a national pilot project aimed to build 600 housing with high-energy performance  [12]. It is a 4-flour building and contains 10 apartments with an area of 67 m 2 each. We have used the TRANSOL software under TRANSYS to calculate the heating (space and hot water) and cooling load. Several parameters are then introduced to size and evaluate the performance of our installation.