dataesr/scientific-paragraphs-categorization huggingface dataset:
Retention time prediction accuracy PredRet provided RT predictions for compounds never analysed in the CSs but also for compounds in the entry dataset. We used the latter to compare prediction accuracy between CSs. RT predictions were highly accurate across the 24 CSs, with median prediction errors between 0. 03 and 0. 76 min (Table  2 ). As run times vary greatly across CSs (5 to 135 min), median prediction errors were also expressed in percentage relative to the total runtime, ranging from 0. 3% to 1. 8% (CS9 excluded). A graph comparing experimental and predicted RTs for compounds of CS1 entry dataset is given in Figure  3  as an example. Equivalent graphs for all other CSs are provided in Figure  S4. In CS1, accurate predictions with narrow PI were obtained for most compounds with RT ranging between 6.
For example, CS7 contained sesquiterpenoids not represented in other CSs, likewise for anthocyanin glycosides in CS8, urolithins and conjugated isoflavone metabolites in CS14, glucosinolates and rare flavonoids in CS17, flavonolignans (e. g. , dehydrosilydianin), rare flavonoids and sulfated conjugates in CS20, and urolithins and conjugated flavonoids in CS24. Adding the RTs of these rare plant food compounds and metabolites contributes to the richness of PredRet database; however, a caveat is that these CSs themselves may not receive the benefit of good prediction coverage. In such circumstances, the user is encouraged to include common plant compounds that are also frequently represented in the PredRet database. As an example, we propose a list of 14 compounds frequently analysed in our study ( 67% of 24 CSs), which covers a wide RT range: 4'-hydroxy-3'-methoxycinnamic acid (ferulic acid), 4-hydroxy-3-methoxybenzoic acid (vanillic acid), 5-O-caffeoylquinic acid, 4'hydroxycinnamic acid (p-coumaric acid), 3,4-dihydroxybenzoic acid (protocatechuic acid), 3',4'dihydroxycinnamic acid (caffeic acid), 3,4,5-trihydroxybenzoic acid (gallic acid), 3',5'-dimethoxy-4'-hydroxycinnamic acid (sinapic acid), (-)-epicatechin, kaempferol, hippuric acid, luteolin, phloretin and hesperetin (Table  S4 ).
To further validate the predictive performance of GAM in PredRet, we performed an external validation test on a subset of eight CSs, splitting the experimental datasets into 80% for training sets and 20% for test sets. The training sets were used to build GAMs between CSs in PredRet database to obtain predictions with PIs for the compounds in the test sets. Predictions were compared to experimental data to obtain the prediction error for each compound (Table  S5 ) and the prediction statistics for each CS are provided in Table  S6. Accurate predictions were achieved, with the median prediction error in the test sets ranging from 0. 04 to 0. 41 min across the eight CSs. The maximum absolute prediction error was 3. 55 min for -tocopherol (CS2), followed by catechol (2. 45 min, CS5). It is difficult to compare the performance of PredRet with other RT prediction tools as those only allow predictions within the same CS, while PredRet predicts RTs from one CS to another CS differing in mobile phase composition, gradient and flow rate.
In addition, as RTs of flavonoid conjugates (glycosides, glucuronides) differ from that of their aglycones, prediction of RT may help to distinguish between aglycones truly present in the samples and detected aglycones that are generated during the analysis as in-source fragments of glycosides or glucuronides. Another useful application of PredRet is aiding in annotation of rare plant food compounds in untargeted metabolomics studies, when the standards are not commercially available or difficult to synthesise. As soon as a user enters experimental data for a rare plant food compound in a CSs, PredRet provides RT prediction with PI for this compound in CSs where it has not been experimentally measured. For example, the contribution of tomatidine's experimental RT (11. 8 min) from CS1 enabled the prediction of RTs in 15 other CSs, while formononetin (CS1), 8hydroxy-urolithin-3-sulfate (CS14) and 8-deoxylactucin (CS7) enabled the prediction of RTs in 13 other CSs.
Conclusion PredRet, based on pairwise GAMs, was demonstrated to be a useful tool for obtaining a good number and highly accurate RT predictions for plant food bioactive compounds and their metabolites. Its use in untargeted metabolomics studies can definitely help for tentative identification, by eliminating hypotheses that do not fall within the predicted RT range, or when commercial standards are not readily available. PredRet predictions are precise enough to distinguish structural isomers. Our data sharing initiative and multi-laboratory study contributed to the expansion of the PredRet database with > 1500 experimental RTs in 24 CSs for > 467 plant food bioactive compounds and their metabolites (> 30 families). Importantly, as more experimentally known RTs are entered, more RT predictions are generated and accuracy of the predictions increases.
The PredRet database has grown considerably since its introduction and now contains 15,000 RT entries across 68 CSs. Overall, the database covers 4,000 unique compounds, beyond plant food bioactive compounds. In comparison, spectral libraries such as the MassBank of North America (MoNa), contain mass spectra for >200,000 compounds, so there remains a large potential for RT sharing. If sufficiently developed to allow accurate RT prediction in any CSs, PredRet would facilitate comparisons between-studies and minimise the need to develop a consensus LC-MS method for plant food compounds. We thus invite the scientific community to contribute to the community-driven open access PredRet database as part of the global effort for annotation of the dark matter of metabolomes. We suggest that sharing of RT as well as collisional cross section data should be as commonplace in the future as sharing of MS/MS data to provide enough orthogonal data for unambiguous identification in metabolomics.
6. 3-VEKOP-16-2017-00005 ). Sonia de Pascual-Teresa thanks the Spanish MINECO  for financial support (grant number  AGL2016-76832-R ). The  MetaboHUB French  infrastructure has supported the  INRAE  platform (PFEM, MetaboHUB-Clermont) involved in this work (grant number  ANR-INBS-0010 ). Dilip K. Rai  acknowledges  Teagasc  for the financial support through the  Walsh Fellowship  (grant number  ANR-INBS-0010) 2016038 ).
This article proposes a novel scheme to improve the doubly-fed induction generator (DFIG) behavior during grid fault. The DFIG's are sensitives to voltage variations when abrupt variations of the wind velocity arrive. For enhancing DFIG behavior, protecting the converters, and smoothing the fluctuations power output of the DFIG under sag voltage; a novel hybrid energy storage system scheme and its controller are proposed. The main advantages of our approach are a faster response and suppressing overvoltage on DC bus and globally less stress in the storage system. The control structure decreases the tiredness on the battery and restores the DC bus voltage rapidly, globally the battery system operating time increases. The results obtained by simulations in MATLAB validate the benefits of the suggested control.
Series grid side converter (SGSC) that it is detailed in  [14] , the dynamic voltage restorer (DVR) is studied in  [15] , superconducting magnetic energy storage and storage energy systems both are characterizing by highly efficient energy storage and power controllability under faults  [16] ,  [17]  having a good result for enhancing the LVRT capacity. The strategies mentioned, act during the electrical grid fault during the sudden variation speed of the wind and remain inactive in the normal operation. In this article, a new structure is suggested for enhancing behavior WT system based DFIG, reducing power fluctuation and improving LVRT capability. The model of DFIG WT and the hybrid energy storage system (HESS) are developed in MATLAB/Simulink; the simulation tests are performing to validate our proposed control.
SIMULATIONS AND DISCUSSIONS For validate the effectiveness the suggested system, the DFIG system with the HESS design is established in MATLAB/Simulink, for checking the effectiveness of the suggested scheme, a wind farm is used, it is consisting of six DFIG system, Table  2  shows the elements of each DFIG. The wind speed variation and the sag voltage are shown in Figure  6  and Figure  7. The Figure  8  show the rotor winding current with the traditional scheme under sag voltage. With the suggested system, the peak of the rotor current has been decreased show Figure  9. With HESS into DFIG system, the active power injected into the network becomes more smoothen with Figure  10 , precisely between the two moments t 1 =0. 8s and t 2 =1. 4s. 1429 damage, with the proposed system this peak has disappeared.
The future of transportation depends on several relevant and challenging research areas that are impacted by 1) environmental constraints, 2) digitization of the services, and of 3) new modes of transport more connected due to the service digitization. The research community needs to collaborate with practitioners to efficiently manage the global transportation that requires a proper coordination between the data provider software (Transport Management Software) and the Operational Research tool in charge of trip definition addressing the whole constraints. This paper presents an original contribution by making easily reachable OR resolution methods for the VRP and its variants for software engineers through a high level API which handles an entire problem and selects by itself the best suited methods to solve it.
The interconnection of modules in the business process models  (Wang et al. , 2018)  of the Supply Chain Management is based on application programming interfaces (API) that define a set of procedure and methods that can be executed by a module through an internet connection. It can be written into any programming language that manages internet access using TCP-IP. Those interfaces are called remotely through internet protocols and allow them to build applications with distant services on servers. Such APIs are designed to provide specific data or services in order to delegate parts of much larger applications. They give access to black-boxes on which developers can rely on to provide an expected behavior. Managing data and performing complex tasks are often involved to specialized entities as it requires otherwise to have developers with appropriate skills or to have access to large volume of data to feed such systems.
This choice of separating these two phases also has the advantage to increase the isolation the service. The Router web service has three main features. First, it must determine the shortest path for a given vehicle to navigate through an ordered set of point. Then, the matrix operation has to return the route time and distance between each pair of points. Finally, the isoline feature give the area which could be reached within a given amount of time or within a limited distance. These operations are synchronously performed. The matrix operation may take an important amount of time for a large set of points, but most of the requests are performed for a very small set of points and could be answered in a very short time. It has to be noted, that it uses by default Open Source and Open Data layers to provide such data.
Currently no mechanism of notification has been put in place. Some questions have to be answered first, as we have no assumption on the use case of this API. The VRP could be sent from one device (the route manager desktop) and the solution could be expected both on the same device or on another one (a driver app). The Optimizer Back End includes every operation performed behind the hood to answer client requests. It is structured into multiple modules. When a  The driver, on its side, receive the set of missions to perform its tour. Fleet may also receive, as the tour progresses, data from the field, both to inform the route manager on eventual issues and the final customers to update the ETA. All these web services could be called independently and may be adapted to a large set of contexts.
Web Application The Web Application is available both as a demonstrator of the technical stack and as fully functional Route Management Software (RMS) distributed commercially (see Figure  4 ). It is a user-friendly application that has been built with the Model-View-Controller (MVC) software design pattern to provide a very low latency but let the user manipulating its data to build its route. The Web application uses a server-side scripting language, which means that the code is executed and interpreted by the server. The client, on its side, only displays generated views with behaviors which trigger controllers on the serverside. The views and the controllers are both exposed on the front end. This has for advantage to allow developers to integrate directly parts the web application, or to re-implement some views to fulfill particular needs.
Peculiar CO and H i structures Sixteen out of the 31 galaxies in these two groups observed were detected in CO, such that imaging data could be obtained at ∼1 kpc scale. Figure  1  displays both CO (color scale) and H i (contours) distributions of two interesting cases (IC 5264 and NGC 4632) that show signs of violent external perturbations (for other group members, see  Lee et al. 2022). IC 5264 (1459G) has an asymmetric CO distribution with CO disk size of 6. 5 kpc in the west and 7. 5 kpc in the east. The CO disk is also more extended toward the south. The H i morphology is similar to the CO morphology, showing an extended H i tail-like structure in the east and a marginally truncated H i disk in the west. IC 5264 is thought to be experiencing both ram pressure stripping and tidal interaction.
Comparisons of SFR and H 2 gas fraction between group and isolated galaxies We compare the SFR and H 2 gas fraction (M H2 /M ) of our group members with those of 240 isolated galaxies (i. e. , the field sample) from the extended CO Legacy Database for GASS (xCOLD GASS;  Saintonge et al. 2017). In Figure  1 (c), the fraction (48%) of the star-forming galaxies in our group sample is similar to that (50%) in the field sample. However, many low-mass members (log (M /M ) < 10) in our group sample have suppressed SFRs (i. e. , below the star-forming main sequence). For the H 2 gas fraction (Figure  1(d) ), our group sample shows a high fraction (57%) of H 2 deficient galaxies compared to the field sample (26%). In particular, most low-mass group galaxies tend to be deficient in H 2. Interestingly, these low-mass galaxies are not deficient in H i.
Acknowledgements. This work was supported by the  Korea Astronomy and Space Science Institute  grant funded by the  Korea government(MSIT)  (Project No. 2022-1-840-05 ). Support for this work was also provided by the  National Research Foundation of Korea  to the grant No. 2018R1D1A1B07048314. BL acknowledges support from the  National Science Foundation of China  ( 12073002 ,  11721303 ,  11991052 ) and the  National Key R&D Program of China  ( 2016YFA0400702 ). LVM acknowledges financial support from grants  SEV-2017-0709 ,  RTI2018-096228-B-C31  ( MCIU /AEI/ FEDER,UE) , and  IAA4SKA  (Ref. R18-RT-3082 ).
The authors thank the Materials Physics and Chemistry Doctoral School (ED397) of Sorbonne University for the funding of Runhe FANG's PhD thesis, as well as the financial support of Région Nouvelle Aquitaine, of the French National Research Agency (STORE-EX Labex Project ANR-10-LABX-76-01) and of the European Union's Horizon 2020 research and innovation program under grant agreement No  875629  ( NAIMA  project). The authors also thank  Emmanuel PETIT ,  Cathy DENAGE ,  Eric LEBRAUD  (ICMCB) and  Antoine MICHE  ( Sorbonne University ) for their technical support.
The Mosaic model used in this Article was composed of routing tiles of 16 × 16 devices in a Mosaic of 11 × 11 tiles. Devices in the routing tiles were programmed to be in the HCS with a probability of 0. 07. Neuron tiles were realized in a 20 × 4 array -this allows four signals from each of the four neighbouring tiles to be received independently, as well as four neurons to connect recurrently amongst themselves within a tile. The resulting skeleton connectivity matrix was then exported to a PyTorch model of an RSNN model to be trained on the MIT-BIH heart arrhythmia dataset  41. Specifically, all of the heartbeats of one patient (labelled as 201 in the dataset) were delta modulated into four spike train channels. These spike trains then served as an effective spiking input layer of the model.
Data points were presented to the model in mini-batches of sixteen. Two populations of neurons in two neuron tiles were used to denote whether the presented ECG signals corresponded to a healthy or an arrhythmic heartbeat. The softmax of the total number of spikes generated by the neurons in each population was used to obtain a classification probability. The negative log-likelihood was then minimized using the categorical cross-entropy with the labels of the signals. The derivative of the Heaviside step function, that is used to rectify the membrane voltage of the LIF neurons into a zero or a one, was approximated using the function 1/abs(V mem -V th)  2  -inline with surrogate gradient training methods  45. After training, the synaptic weights were transferred into a an array of 16 kb resistive memory devices co-integrated onto a 130 nm CMOS technology.
The synaptic weight of each synapse was defined by the subtraction of two conductance states of two devices. The process of transferring the high-precision software weights to the conductance states of the devices in the array was achieved using an iterative closed-loop multilevel programming algorithm. It is based on adapting the SET programming compliance current to obtain a conductance within a target range  24  and programming a device until its conductance falls within a pre-defined margin of tolerated error. Such an approach allows each device to be programmed with ten non-overlapping conductance levels. The delta modulated test data was processed by a mixed hardware-software Moasic model. Whenever a pre-synaptic neuron emitted a spike in this model, the corresponding pair of devices (storing the synaptic weight connecting it to the post-synaptic neuron) are read in the RRAM array.
Preparation of the ECG dataset The ECG dataset was downloaded from the MIT-BIH arrhythmia repository  41. The database is composed of continuous 30-minute recordings measured from multiple subjects. The QRS complex of each heartbeat has been annotated as either healthy or exhibiting one of many possible heart arrhythmias by a team of cardiologists. We selected one patient exhibiting approximately half healthy and half arrhythmic heartbeats. Each heartbeat was isolated from the others in a 700 ms time-series centered on the labelled QRS complex. Each of the two 700 ms channel signals were then converted to spikes using a delta modulation scheme  49. This consists of recording the initial value of the time-series and, going forward in time, recording the time-stamp when this signal changes by a pre-determined positive or negative amount.
16. Vortex Mixer (Model Number: K-MI0101002D)  17. pH meter (PHS-3D-01)  18. Eppendorf MiniSpin Plus Microcentrifuge (CAT# 5453)  19. Precision balance (Cat # W3200-320) Software Bioinformatic softwares used for the development of specific primers. 1. Genome sequences were retrieved from NCBI GenBank ( http://www. ncbi. nlm. nih. gov/genome/ )  [1]. 2. Coding sequences for housekeeping genes were manually extracted with the Artemis genome browser ( http://www. sanger. ac. uk/science/tools/artemis ) [2] 3. Sequence alignments were performed using the MUSCLE algorithm ( https://www. ebi. ac. uk/Tools/msa/muscle/ )  [3]. 4. LAMP primers were designed using Primer Explorer V5 ( http://primerexplorer. jp/lampv5e/index. html , Eiken Chemical Co. , Japan). 5. Primer specificities were tested using Primer-BLAST at the NCBI website ( https://www.
Acknowledgments This protocol has been realized with the financial support of the  Allocation de Recherche pour une Thèse au Sud (ARTS) program  of the  Institut de Recherche pour le Développement (IRD). Dr Kossi  KINI received an individual research grant  N °C/5921-1  from the  International Foundation for Science (IFS). The  Africa Rice Center (AfricaRice)  and the  IRD  provided financial support from the  Global Rice Science Partnership (GRiSP). In addition, AfricaRice provided financial support obtained from the  Ministry of Foreign Affairs, Japan. All these institutions are gratefully acknowledged.
Muscle sectioning Muscle biopsies were collected from one biceps brachii muscle at baseline and then from the contralateral biceps following part 2 of the NCT02310906 study after 48 weeks. Serial unfixed frozen Sects. (5 μm-thick) were cut from baseline and 48 weeks muscle biopsies from all 25 boys involved in the SKIP-NMD 4053-101 trial using a Leica CM 1850 UV cryostat (Leica Biosystems, Germany). 3 × serial sections were collected on each slide and slides were stored at -80 °C until time of use. Only 1 muscle sample from each biopsy was analysed for this study as previous work found no difference in dystrophin expression between two adjacent muscle biopsy (samples A and B) from the original reporting  [19].
Results 3 × serial sections of baseline and 48-week biopsy samples from all 25 patients previously acquired in the SKIP-NMD 4053-101 study (NCT02310906) were immunostained for laminin α2, dystrophin and a tertiary protein marker (α-sarcoglycan/β-dystroglycan or f/d myosin). The levels of dystrophin and these tertiary protein markers were then quantified at both time points via digital image analysis. A variety of parameters were then assessed for the tertiary protein markers including changes in sarcolemmal fluorescence intensity, sarcolemmal fluorescence intensity in dystrophin positive and dystrophin negative sarcolemmal regions, % positive myofibres for each protein and finally their correlation with levels of dystrophin.
Limitations Whilst pre and post treatment sample pairs were blindly evaluated throughout the course of data acquisition and analysis, untreated sample pairs are not available for this study and thus and we are unable to compare with patients who did not receive therapeutic intervention. Furthermore, whilst some evidence of the molecular functionality of induced dystrophin following a sustained period of therapeutic intervention is provided, it is important to note that dystrophin has multiple molecular functions, and only some were assessed in this study. Furthermore, the DAPC contains a multitude of proteins and protein binding interactions. Analysis of more DAPC members including β, γ and δ-sarcoglycan, α-dystroglycan and nNOS would be necessary to reveal more information about the comprehensive picture of DAPC restoration following treatment.
Acknowledgements This work was supported by the  National Institute for Health Research Biomedical Research Centre at Great Ormond Street Hospital for Children NHS Foundation Trust and University College London  and the  MRC Centre for Neuromuscular Diseases Biobank , supported by the  National Institute for Health Research Biomedical Research Centre at Great Ormond Street Hospital for Children NHS Foundation Trust and University College London. We would additionally like to thank the  Centre for Cell & Molecular Dynamics part of BCLEM-Bloomsbury Consortium for Light and Electron Microscopy  for their technical assistance throughout this project. We would also like to acknowledge and thank  Lee Martindale  for his continued assistance and support. Additionally, we would like to thank  Diane Frank  for her valuable insights into this project.