dataesr/scientific-paragraphs-categorization huggingface dataset:
CellCnn analysis Identification of a Covid-19-specific cell-identity signature was carried out using the CellCnn algorithm  (28) , 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. The first 39 samples at day0 were analyzed by CellCnn. Data from each CyTOF panel was analyzed separately, in each case using all measured protein markers to train a series of CellCnn networks with varying hyperparameters. Each sample was given a label corresponding to the Covid-19 status of the patient from which the sample was drawn (positive or negative). To generate input data for training CellCnn, sub-samples of 2000 cells, termed multi-cell inputs (MCIs), were chosen randomly from each sample independently.
These 2 metaclusters were manually splited into 2 new metaclusters (identified respectively as Mo180, Mo181 and Mo214, Mo243) (Fig. S1B ). Thus, altogether we analyzed 15 metaclusters of myeloid cells. Regarding the lymphoid compartment, we noticed that FlowSOM defined metaclusters at the lineage level, thus we retain all the 136 clusters included in 10 metaclusters of interest (i. e. containing lymphoid lineage markers) (Fig. S1C ). All metaclusters and clusters phenotypes including their abundances and mean marker intensity were then exported from Cytobank for further analyses. Cytometry data was explored with Kaluza Analysis Software (Beckman Coulter). Hierarchical clustering and heatmaps were generated with R v3. 6. 3, using Rstudio v1. 2. 5033 and the pheatmap package.
Statistical analysis Statistical analyses were performed with Graphpad Prism 8. 4. 3. P values were defined by a Kruskal-Wallis test followed by a Dunn's post-test for multiple group comparisons or by Wilcoxon matched-pairs signed rank tests as appropriate. Correlations were calculated using Spearman test. * P < 0. 05, ** P < 0. 01, *** < 0. 001, and **** P < 0. 0001. Hierarchical clustering of the patients was performed using euclidean distance and complete clustering. Correspondence analysis was performed using the package factoshiny using as variable the abundance in cell subsets for each patient.
Starting with a large set of synthetic attC sites, we evolved them to encode a protein sequence with desired properties, here, encoding peptides with sequences similar to those of unstructured linkers from the database  (17). We then selected three synthetic sites attC L1-3 that obtained the best scores (Fig. 3 , A to C, and Supplementary Materials). Like other synthetic attC sites, they were all functional in recombination, with attC L1 and attC L3 sites recombining with more than 10 -1 frequency (Fig. 3D ). To test whether the resulting sequences were also functional as peptide linkers, we used them to fuse two domains of Bordetella pertussis adenylate cyclase, used as a bacterial two-hybrid system  (18, 19). The cyclic adenosine monophosphate (AMP) production by the enzyme requires a close interaction between both domains and can be measured by an enzymelinked immunosorbent assay (ELISA)-based assay (Supplementary Materials).
Machine learning predictions of attC site recombination To analyze the results corresponding to 162 single and 12,879 double attC r0 mutants, we decided to use a machine learning (ML) approach. Our objective was twofold: to test whether a regression algorithm  (23)  could predict the recombination frequencies of attC sites based on their sequence and structure and, if so, to deduce the properties on which these predictions are based. A regression algorithm is a supervised ML technique for estimating an unknown continuous function based on a finite number of data points. Here, a data point is an attC r0 mutant, the input is the set of sequence-and structure-related features, and the output is the enrichment value. Once the model is learnt from the training dataset, it can be tested on the unseen data (test dataset) to predict the output, and its performance can be evaluated.
To use regression algorithms, we first needed to define a number of features to describe each attC r0 mutant. Since the integrase recognizes the structure of the bottom strand of attC sites (6), we based our features on the predicted folding of the bottom strand of each mutant using the RNAfold program from the ViennaRNA 2. 1. 8 package (Supplementary Materials)  (24). Our list included several global features describing attC site folding, such as Gibbs free energy (∆G) of the structures, the probability to fold a functional structure (pfold), i. e. , a structure with correctly folded integrase binding sites  (25, 26) , and others. It also included a set of characteristics for each particular base, such as the nucleotide present at that position, its pairing probability, and positional entropy.
The positional entropy of a base reflects how unstable it is: Low positional entropy means that the base is stabilized in only one major state (either paired with a particular base or unpaired), whereas high positional entropy means that the base can be found in various states within the thermodynamic ensemble of possible structures. As a second step, we prepared the data for ML by discarding features with zero variance, normalizing feature and enrichment values, and equilibrating our dataset to contain an equal number of enriched and depleted mutants (Supplementary Materials). Last, we used four different regression algorithms: decision tree regression  (27) , ridge regression (28), support vector regression  (29) , and random forest regression   (30) , available in the Python scikit-learn library  (31).
To evaluate their performance, we ran every algorithm 50 times, each time splitting the data points into training and test datasets and performing fivefold cross-validation. Among the four algorithms, random forest regression showed the best performance, attaining a Pearson r = 0. 81 (Fig. 5 A T C G T G C T G C C T A A C A C T C T T T A C C A T G G T T C G A G G A A C T C G A C A T G A A C C T C C G T T A G G C A G 5′ 3′ R box L box UCS VTS Stem S C I E N C E A D V A N C E S | R E S E A R C H A R T I C L E 5 of 9.
Biological interpretation of ML results Since the ML algorithm performed well in cross-validation tests on the dataset of attC r0 mutants, we concluded that it did not overfit the data but instead "learned" the relationship between the features of attC sites and their recombination frequencies. We tried to deduce these relationships by analyzing features that were most relevant for the prediction. None of the feature selection methods succeeded in identifying a set of features sufficient to construct a good predictive model, suggesting that most of the features were meaningful. However, random forest regression algorithm allows ranking features according to their relevance to the model, using the feature importance measure that assigns higher scores to the most important features (table  S3C ).
ML analysis of large-scale mutational datasets ML is recognized as one of the most efficient methods for analyzing complex problems  (43)  and is rapidly gaining importance in bioinformatics  (44). Since attC site recombination is a multifactorial problem, the use of ML was a compelling strategy to construct a predictive model of this biological function. Here, ML allowed us to achieve both of our objectives: We constructed a predictive model of attC site recombination frequency and were able to draw biological conclusions from this analysis. However, this approach also has its limitations: The interpretation of ML performance is known to be nonstraightforward, and its generalization is not always possible. To overcome this limitation, we performed feature importance analysis to make hypotheses that were then experimentally tested.
Acknowledgments: We would like to thank  M. Weigt  for fruitful discussions and  D. Ladant  for providing vectors for the ELISA-based linker assay, as well as  C. Bouchier  and  S. Kennedy  from the  Biomics Pole of Institut Pasteur  for performing the sequencing and for general help. Funding: This work was supported by  Institut Pasteur ,  Centre National de la Recherche Scientifique, French Government 's  Investissement d'Avenir program Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  ( ANR-10-LABX-62-IBEID ), the  French National Research Agency  ( ANR-12-BLAN-DynamINT ),  Paris Descartes University, and Ecole Doctorale Frontieres du Vivant, Fondation pour la Recherche Medicale  ( FDT20150532465 ).
Then, in section 3, we present our proposed Solution in terms of the network architecture and the protocol stack in the ED followed by the communication procedure and handover steps. In section 4, we specify the steps taken by ED to alternate between the two technologies in three scenarios: Connection lost, Handover initialized by the network and Handover initialized by the ED. In section 5, we show the contributions in terms of supporting switching between heterogeneous networks and technologies, and granting the use of IPv6-based solution which results on saving the time required to deliver the data by optimizing the communication routing and saving the bandwidth. In addition, in section 6, we highlight the cases within the same technology by improving LoRaWAN roaming and decreasing the registration process of NB-IoT ED to access the network.
Upper-Level This level has to manage the mobility and select one of the technologies found on L2 layer (data link layer). This level contains three blocks: • Protocol stack: it is formed of the protocols UDP and light implementation of Mobile IPv6 (LMIPv6). Other than these three protocols shown in fig. 8  can be used while considering the ED limitations in term of hardware resources and restrictions of the radio. The protocol stack block is responsible for the management of mobility using LMIPv6 and the continuity of the session with AS after the handover between networks. • Technology Selection Algorithm (TSA): it is responsible of the selection of the technology as detailed in  [19]. • Database: it contains the context for compression and decompression (C/D) of the protocol stack headers.
• Direction Indicator (DI): Used to specify the direction of the packet, whether uplink (Up); data are from device to server, downlink (Dw); data are from the server to device, or Bidirectional (Bi). • Target Value (TV): This field contains the value that ED receives to compare with the saved value. • Matching Operator (MO): This operator is applied in comparing received TV with saved one. • Compression Decompression Action (CDA): It describes the method used beside each field to compress and decompress the TV. The configuration parameters are obtained from the database to configure DCHC when TSA selects a technology. After each configuration, DCHC reforms the data received from the protocol stack block to adapt the payload frame of the technology used at lower-level.
When TSA decides to change, it updates the database with the received parameters and then sends a command using the ALCS to the ALC layer. Then, ALC obtains the new database parameters to reconfigure the DCHC to work with NB-IoT technology. During this step, the TSA pauses the transmission of the running applications and waits to receive an event indicating that the radio is ready. After reconfiguration of DCHC, the protocol stack receives the event, then the registration process begins with the NB-IoT network. In LPWAN, the ED does not have a disconnection procedure with the previous network. Also, it does not have to inform the network about the handover. Once the device is registered with the NB-IoT base station, the network informs MMS that the switching process was successful. After that, the MMS sends the context of the device used to decompress the received packets and stores them in the core network.
ED initialization Consider that the ED is initially connected to the NB-IoT network. As shown in fig. 15 , the device detects the degradation in RSS using the ALES. Then, TSA consults the MMS about available technologies close to it  [7]. When MMS receives the request from ED, it queries the LoRaWAN network to verify the available resources. After that, the MMS sends the device information including the frequency, spreading factor, etc, that should be used. Then, the TSA updates the database on the ED, and sends a command to the ALC layer to update DCHC and switch to LoRaWAN technology. ALC verifies the database to obtain the new parameters to reconfigure the DCHC layer. At the end, an event rises to the upper layers indicating that the link layer is ready to use. After successful registration with LoRaWAN network, the network server updates the MMS with the location and parameters of the ED.
Communication scenario Before the ED leaves the coverage of the NB-IoT network, the status of the ED changes from "IDLE" to "connection resume", as shown in fig. 7  and explained in section 4. This is to get ready to transmit the data. The ED packs the data in the form of an IPv6 packet. This packet goes through DCHC  [20]  that compresses the headers as shown in Table  2. In this table, we illustrate the compression of IP packet headers at the network layer with the context (NLC). As the ED is assigned to the NB-IoT network, the context of the headers is not changed and it is saved in the network. As shown in Table  2 , the ED only sends the rule number without any headings. Thus, Rule 1 matches all the used headers  [5]. The size of Rule 1 is one byte as explained in section 3  [21]. However, the compressed header is empty, since the matching operator (MO) is ignored.
Then, we consider that during the ED movement, it detects that the RSS value with the NB-IoT GW has decreased. Therefore, ED transmits a message in step two to MMS asking about the available networks in the nearby region that support better communication. After that, MMS contacts LoRaWAN network servers that cover EDs geographical location with its GWs. Then, MMS responses by providing information about the available networks. The format of the reply message is the same as in the table 2 with the source and destination address headers exchanged. During this procedure, MMS updates the context and pre-registrations (at the data link layer) for ED in the LoRaWAN network and sends the new identity to ED in addition to CoA compatible with the network. Upon receiving the response, and before leaving the NB-IoT network, the TSA block of the ED sends a command to the middle level layer to change the link layer to LoRaWAN as explained in section 4.
At the data link layer, ED takes the new identity and saves the keys for communication with LoRaWAN network. At the network layer level, ED selects one of the CoAs found in the response message and uses it as a new L3 identity (at the network layer) for the link update procedure as shown in step three of fig. 16  (see green line). Then, the ED transmits the first LoRaWAN frame with compressed headers. As shown in Table  3  the CoA is the source address, the AS address is the destination address and the HoA is set in the extension headers. Thus, we divide the table into two parts; the main context in the NLC and the extension context in the extension layer context (ELC). This packet will be named as handover packet, since it is the first packet transmitted after handover between NB-IoT and LoRaWAN.
The shape of the transmitted LoRaWAN frame is shown in fig. 17 , where the PHY payload contains the RuleID (1 byte), compressed headers (n bytes), and the data set in the PHY payload. The values sent in Table  3  are set in the Compressed Header field. After decompressing the payload of the LoRaWAN frame, the IP packet is reformed and sent over the network. Following the decompression, the visited operator can use the routing optimization method  [5]  to communicate directly with AS without going through the NB-IoT network as shown in fig. 16  (see blue color).
Then, the ED transmits the data. This process costs 158 ms in our simulator to change, obtain database values and configure. But, we will not consider the moment of change, since it runs on a PC and not on a real ED. In this experiment, MMS contributes to the handover in  the data link layer, while compressed LMIPv6 contributes to the continuity of the communication with AS after the ED identity changes at L3 layer. In summary, the proposed framework saves time of the ED consumed in the lost connection state and prevents loss of connection. Actually, in a real case, the speed of the ED installed in the truck should be considered. Therefore, in a network without MMS, a delay of 16 seconds followed by 6 seconds to register with the LoRaWAN network or 10 seconds with the NB-IoT network is unacceptable with high speeds.
Results In position P1, as shown in fig. 19 , the ED is under GW(A) coverage. As shown in Table  4 , it transmits a Lo-RaWAN frame to GW(A). Then, the LoRaWAN frame is forwarded to the H-NS. In the H-NS, the identity is verified, then the LoRaWAN frame data is unpacked and sent to AS. In this position, ED communicates with the H-NS, so MMS has no contributions with the registration (ABP or OTAA). Therefore, the ED can deliver data to AS. The total communication time was 2162 ms. In position P2, as shown in fig. 19 , the ED is under   4 , it transmits a LoRaWAN frame to GW(D). This transmission is 1,50938 seconds. Then, GW (D) forwards the LoRaWAN frame received to V-NS(X). The link between GW(D) and V-NS(X) lasts 50 ms to resend the received LoRaWAN frame. Since the collaboration with the H-NS is passive, the V-NS(X) verifies only the identity of the device in the L2 layer and sends the LoRaWAN frame back to the H-NS.
Because it is not necessary to change the identity, the ED (ABP or OTAA) can deliver data to AS. The total process is achieved in 2738 ms. This additional time due to the cost of the link between the two servers. In position P3, as shown in fig. 19 , the ED is under GW (F) coverage. As shown in Table  4 , when the network does not support MMS, the ED that was activated using ABP can no longer deliver data to AS. In addition, when ED uses OTAA, the LoRaWAN frames are droped by V-NS(Y) and unacknowledged. Then ED detects that there is no coverage while acknowledgement not received. Therefore, this ED performs the registration process and retransmits LoRaWAN frame. The total process costs 11573 ms to deliver the data to AS. While, in an MMS-compatible LoRaWAN network, the device obtains a new identity and keys before losing the connection with the V-NS (X).
Simulation Since an NB-IoT network is not available to perform our tests, we used the NS3 software to implement our test scenario. NS-3 has a complete LTE module that includes EPC control and data plane, an end-to-end data control plane protocol stack, a physical layer data error model, radio propaga- tion models and different MAC scheduler. Therefore, based on the LTE module in NS-3, the authors in  [28]  implemented NB-IoT modules where they adapted the LTE module according to the NB-IoT standards. LTE had been modified in terms of the PHY layer, the MAC layer and the scheduler to meet the 3GPP specifications of the NB-IoT. But, a crucial part of NB-IoT has not yet been involved in  [28] , such as the random access of NB-IoT, which is the main part of our test. In  [23] , authors implemented NB-IoT Random Access (RA) in their project.
Materials and methods The present systematic review was conducted according to the PRISMA guidelines. The literature search was conducted in October 2019. All clinical trials using the semitendinosus and/or gracilis tendon grafts for isolated MPFL reconstruction in patients with patellofemoral instability were considered for inclusion. Only articles reporting a minimum of 12-month follow-up were considered. The PEDro score was used for the methodological quality assessment. Results Data from 1491 procedures were collected. The mean follow-up was 36. 12 months. There was comparability among the patient baseline. All the scores of interests (Kujala, Tegner, Lysholm) and range of motion scored better in the semitendinosus group. Moreover, in favour of the semitendinosus group, a statistically significant reduction of the revision surgeries and re-dislocations were evidenced.
Literature search Two independent authors (FM, JE) performed the literature search. In October 2019, the following databases were accessed: Pubmed, Embase, Google Scholar, Scopus. The following keywords were used isolated or in combination: patellofemoral and/or patellar combined with instability, dislocation, luxation, syndrome combined with MPFL and/ or rupture, tear, reconstruction combined with semitendinosus, gracilis, hamstring, tendon, graft, combined with bundle, doubled, single. The same authors screened the resulting articles. If the title and related abstract matched the topic, the full text was accessed. Furthermore, the bibliographies were screened to find additional articles.
Eligibility criteria All studies reporting the outcomes of MPFL reconstruction using the semitendinosus and/or gracilis tendon graft for recurrent patellofemoral instability were considered for inclusion. According to the Oxford Centre of Evidenced-Based Medicine  [11] , levels of evidence I to IV were included. According to the authors' language capabilities, articles in English, German, Spanish, Italian and French were included. Only articles reporting data concerning isolated MPFL reconstruction that were included with a minimum of 12 months of follow-up were considered. Only articles reporting quantitative data under the outcomes of interest were considered for inclusion. Techniques, comments, letters, editorials, protocols and guidelines were excluded. Biomechanical, animal and cadaveric studies were also excluded.
Identification of efficient origins in mouse ES cells using OK-seq Replication origins/initiation zones are needed for analysis of the eSPAN datasets. Therefore, we profiled replication origins in mouse ES cells using Okazaki fragment sequencing (OKseq)  (13). We identi fied 2591 and 2288 initiation zones in two independent replicates, respectively, with 1835 of them overlapped (fig. S1A ). In addition, the OKseq bias profiles had a good correlation with published datasets both genomewide and around origins (fig. S1B )  (26) , indi cating the reproducibility and reliability of our identified origins. For the downstream analysis, we selected 1548 origins whose ge nomic localization is less than 3 kb apart in the two repeats. These origins showed a strong lagging strand bias in both repeats and were ranked on the basis of replication efficiency (fig.
Analysis of H3K36me3 CUT&Tag/ ACTseq datasets in both WT and POLA12A mutant cells indicates that H3K36me3 CUT&Tag/ACTseq peaks correlated strongly with published H3K36me3 ChIPseq signals genome wide (fig. S5,  D  and E )  (40) , suggesting that our method and the H3K36me3 antibody used are reliable to detect its chromatin locations. H3K36me3 eSPAN peaks in WT cells displayed a weak bias toward the leading strand, and the bias was markedly increased in POLA12A mutant cells (Fig. 3, A, D,  and G ). Moreover, the H3K36me3 CUT&Tag/ ACTseq peaks had no bias in either WT or POLA12A mutant cells (fig. S5F ). Last, the levels of H4K20me2, H4K12ac, and H3K36me3 around origins were not altered markedly in POLA12A mutant cells compared to WT (figs. S3D and S5G). Together, these results provide additional evidence that POLA12A mutant cells affect the transfer of parental H3H4 to lagging strands.