dataesr/scientific-paragraphs-categorization huggingface dataset:
To interpret the results and compare them, let us first note that in Figure  3 , a 0 error means perfect prediction, while an error of 1 means that no information is extracted from the input. Indeed, a function outputting always the same value, for all samples, can at best predict the average target value over the dataset, in which case the error is the standard deviation over the dataset of the value to predict, which is normalized to 1 in our setup. Processing the SNP and distance matrices with a MLP led to high prediction errors, especially for recent population sizes. This is not surprising, since genomic information is encoded as a simple list of values, where the order has no meaning from the MLP point of view, which then cannot exploit information given by the data structure. In summary, an MLP configuration has several drawbacks: (i) the number of network parameters to estimate is high; (ii) the MLP can only retrieve the geometry of the data through training, with no guarantee that it will learn the spatial structure of the genome (i.
Robustness to the number of individuals Importantly, SPIDNA adapts to the number of individuals, which is an advantageous property compared to many methods relying on summary statistics. SPIDNA can be trained on data sets having similar or varying sample sizes, and, once trained, it can be directly applied to a dataset of reasonably close sample size, but unobserved during training. We provide an example of robustness in an experiment focusing on a subset of demographic scenarios (decline, growth, medium or large constant size) and a wide range of sample sizes (from 10 to 150, Figure  S5 ). SPIDNA using batch normalization (trained on exactly 50 individuals) did not suffer a strong loss of accuracy when the sample sizes remained in the  [45, 65]  range. Outside of this range, the predictions were inaccurate in two cases: small sample sizes under expanding and constant size scenarios, or large sample sizes under the expansion scenario.
Combining ABC with SPIDNA thus adds a local inference step to the general mapping learnt by SPIDNA, and this might help readjust the predictions locally. This is illustrated in Figure  4  where recent population sizes estimated by SPIDNA have a tendency towards the center of the prior while SPIDNA+ABC corrects it. This combination might be modifying the bias/variance trade-off favored by SPIDNA towards higher variance. These hypotheses could be investigated further in future works. This gain however comes with a disadvantage which is the need for ABC to approximate a posterior distribution for each new dataset. This can be fairly time consuming for large panels containing many populations for which demography has to be reconstructed. Contrary to ABC, SPIDNA and other deep learning approaches, once trained, provide immediate predictions.
This amortization of the training time is relevant for all studies processing large number of datasets such as meta analyses over populations or species (e. g  Roux et al. (2016) ) or addressing window-based tasks, such as selection and introgression scans, local ancestry or recombination estimations. In these cases the parameter predictive uncertainty could be estimated by the network  (Chan et al. , 2018 , Lakshminarayanan et al. , 2017)  rather than through an ABC procedure. Finally, we showed that applying ABC to SPIDNA predictions combined with precomputed summary statistics led to an error 4. 7% smaller than the one of a regular ABC and 6. 0% smaller than SPIDNA. This indicates that the information retrieved by SPIDNA does not completely overlap the information encoded in the predefined summary statistics but is not completely orthogonal either.
Application to real data Applying a method trained on simulated data to a real dataset can be a difficult task. Here we show that the estimated effective population sizes of the three cattle breeds were qualitatively similar across the different methods used. All of them were able to recover the large ancestral population size shared by the three breeds, followed by its decline after domestication. However, the methods produced size estimates that were quantitatively different, notably in the strength of the decline and the recent population sizes. For quality reasons, inference was done using genotypes pruned of low frequency alleles rather than haplotypes. The architecture and hyperparameters were optimized based on simulated haplotypes, and the network was trained on simulated genotypes.
and applied the same treatment to the simulated training set. We split the real data of each breed into 2Mb and removed segments comprising centromeres leaving 1,213 segments. We obtained a similar number of SNPs for the three breeds: Angus (average: 4,536 SNPs, maximum: 22,391 and minimum: 775), Fleckvieh (average: 4,837 SNPs, maximum: 24,896 and minimum: 896) and Fleckvieh (average: 4,732 SNPs, maximum, 24,098 and minimum: 1,212). Then we trained ABC, SPIDNA and a combination of both with the best hyperparameter configurations on the modified simulated data and performed the inference. The best version of SPIDNA without ABC is non-adaptive and therefore uses 400 SNPs from each segment which represents 10% of the total number of SNPs in the cattle data and 67% for the training dataset.
Acknowledgments We are grateful to the genotoul bioinformatics platform  Toulouse Midi-Pyrenees  ( Bioinfo Genotoul ) and the  TAU team  for providing computing and storage resources, to the  Paris-Saclay Center  for Data Science 2. 0 ( IRS ) for funding. JC salary was funded by DIM-1Health. We are also grateful to  Simon Boitard  for helpful discussions and providing the cattle dataset. We thank  Michael Blum  for his comments on a first version of the manuscript and  Diviyan Kalainathan  for his support with the Titanic platform. We also thank  Jeffrey Spence ,  Lex Flagel  and two anonymous reviewers for their comments.
5G-U model validation In the numerical and simulation results presented next, we consider 5G-U system with parameters as defined in the latest IEEE 802. 11 systems operating on the 5GHz unlicensed band, regarding the time slot duration T s , the backoff periods SIF S, DIF S and bit rate R b. The transmission uses the whole available unlicensed band of spectrum, hence only one transmission can take place in a given time. The data packet size including all headers is denoted by L data and the acknowledgment packet size by L ack. The station receives the ACK/NACK after a duration of SIFS, then all stations backoff during a period of DIFS before starting to contend again for medium access. The transmission duration x is then calculated as: x = (L data +L ack )/Ra+SIF S+DIF S Ts , where. is the ceiling function.
The system deploys LBT-cat3 with a fixed contention window size of W 0. We consider that the station generates a packet every 10ms following a Poisson distribution, then we can estimate the probability of packet arrival q per T s. The latency and reliability requirements are set to T = 1ms and R = 1 -10 -5 , respectively. The numerical values are shown in Table  1. T s 9µs R b 100M bps x 7 SIF S 16µs L data 32Bytes W 0 16 DIF S 34µs L ack 14Bytes q ≈ 0. 001 We compare the results obtained from our analytical model with the results obtained from simulation. We build an event-driven simulator using MATLAB with time step of T s. We designate a station to which we estimate p c and P U loss. This station generates a packet as soon as its previous one quits the system, unlike the remaining N -1 stations which generate packets in a given T s following a Poisson distribution of probability q.
2. We assume K = 5 RBs and we evaluate P L loss. We compare the results obtained from the analytical model with a system simulation realised using MATLAB. The simulation output is calculated from the viewpoint of a designated station which is always active, the other N -1 stations generate a packet every T time with probability P a ≈ 0. 1. We trace the curves obtained from analysis and simulation for different values of N in Figure  7 (a) and observe a good match. We notice here that K = 5 is not enough to guarantee the reliability requirement when using 5G-L alone, where ∀N : P L loss > 10 -5.
Comparative genomics is the analysis of genomic relationships among different species and serves as a significant base for evolutionary and functional genomic studies. GreenPhylDB (https://www. greenphyl. org) is a database designed to facilitate the exploration of gene families and homologous relationships among plant genomes, including staple crops critically important for global food security. GreenPhylDB is available since 2007, after the release of the Arabidopsis thaliana and Oryza sativa genomes and has undergone multiple releases. With the number of plant genomes currently available, it becomes challenging to select a single reference for comparative genomics studies but there is still a lack of databases taking advantage several genomes by species for orthology detection. GreenPhylDBv5 introduces the concept of comparative pangenomics by harnessing multiple genome sequences by species.
It became obvious that distinguishing core from dispensable genes is important as dispensable genes can be associated with useful trait diversity  (10). Finally, PAVs can have an influence on orthology detection as specific genotype gene losses can lead to false negative results in interspecific comparisons or to pseudo-orthology  (19). Until now, comparative genomics databases have not fully taken advantage of these new datasets. Here, we present an updated version of the GreenPhylDB, a database that features multiple genomes for 19 species (e. g. rice, maize, banana, grape and cacao) as well as 27 other species with single reference genomes, for a total of 46 genomes. Publicly available genomes were processed to generate representative pangenes (i. e. a set of representative or consensus sequences) for species that were used in multi-species sequence clustering.
Sequence retrieval and quality checks We retrieved 132 publicly available datasets (coding DNA sequence and protein-coding genes) for 46 (Supplementary Table  S1 ) and assessed their gene annotation predictions using BUSCO Plants v3. 0. 2 (embryophita odb10)  (20). We checked that the number of CDS was consistent with the number of proteins and that they shared the same locus tag name. When protein-coding genes were missing, we generated the sequences from GFF files originating from data providers. Finally, alternate splices were filtered, and the longest sequence was conserved.
Pangene construction Out of the full dataset, 105 genomes were considered to produce 19 pangenes computed with the get homologues-est software v20092018 (21) (Table  1 ), based on NCBI Blast-v2. 2 and using the following program options (-M -F -t 0 -m cluster) as previously applied to the Brachypodium distachyon dataset  (13). We processed each cluster the following way : i. For single-gene copy clusters (a single sequence per genome), protein sequences were aligned using MAFFT v7. 313  (22)  (parameters adjusted according to the number of sequences) and an automatic procedure to generate a consensus sequence was applied (Figure  1A ). For each position of the alignment, we kept the most frequent amino acid. In case of a tie, the amino acid of the genome with the highest BUSCO scores ('complete' then 'fragmented' and finally lowest 'missing' scores) was selected.
Large gene families can include several multi-copy clusters being grouped together at the sequence clustering step. iii. For genotype-specific clusters (paralogs in a single genome), we generated a distance matrix between all sequences and the sequence with the lowest average distance (min(d/sum(d))) of all sequences was putatively considered as the most representative sequence. Those sequences were added to the pangene. iv. Finally, singletons (cluster of one sequence) were searched for similarity using DIAMOND  (23)  with a default e-value on the protein-coding genes of all other species genomes to predict their putative prediction accuracy. Sequences with a minimum of one hit in at least two species were added to the pangene; otherwise sequences were excluded. As a unique identifier was required for each pangene, we defined a nomenclature with a prefix composed of the [5-letter UniProt taxonomy database code] pan, followed by p (for protein) and an auto-increment of 6-digits (e.
Manual curation and sharing of gene families While automatic clustering is a relevant and efficient starting point, sometimes limitations (e. g. missing sequences, er-rors in gene annotations) are present and prevent access to ready-to-use datasets, justifying a deeper characterization that will eventually lead to a refinement of the automatic clustering. As a result, knowledge generated on individual gene families is often available only in publications and their supplementary information as PDFs. To encourage knowledge capture, we developed a section for advanced users to create and share their own gene families. Two methods are possible: users can either start from scratch and upload their data or use existing clusters and take advantage of multiples operations implemented in the 'MyList' features: such feature was indeed developed to facilitate intersecting, combining clusters.
CONCLUSION This new version of GreenPhylDB provides a unique way to scale up plant comparative genomics studies across multiple plants species by leveraging pangenomic datasets. This release paves the way to the transition from reference-based genomics to pangenome-based systems and tools. In this context, the website includes new powerful search interfaces to explore the content of the gene family collection. Advanced users can also deposit the results of their expert gene family curation for further use and reference. GreenPhylDB is an important resource to understand the genetic basis of genome diversity among plant species and has the potential to accelerate gene discovery to support crop improvement.
ACKNOWLEDGEMENTS This work was technically supported by the  CIRAD-UMR AGAP HPC Data Center of the South Green Bioinformatics platform  (https://www. southgreen. fr/). We thank the scientists who facilitated access to some genome datasets:  Stéphanie Bocs  (CIRAD) and  Darlon Lantican  ( UPLB ) for coconut,  Ming Li  ( SAAS ) for wild sweet potato,  Stéphane Deschamps  and  Helen Lin  ( Corteva ) for sorghum. We also acknowledge community databases which provided access to the genome datasets, thus facilitating the development of this online resource.
The methodology used to carry out this work is divided into three complementary stages. In the first stage, we collected key performance indicators, based on a literature review, and conducted a student opinion poll. Once the data were classified into different categories, we ranked these criteria in order of importance using statistical analysis (Stage 2). In the third stage, we implemented a model (function) for housing evaluation; this model of evaluation allowed us to determine a final rating considering the performance of each criterion as well as its importance. Once these various stages were completed, a simple tool was created, enabling the global visualization of the data and various assessment categories and indicators. The objective of the new tool is to allow students to assess different housing units and select the best options.
Several recruitment campaigns took place in classrooms or in front of the University restaurants using paper flyers or tablets at Liege University, the Catholic University of Louvain, and the Free University of Brussels, during lunchtime. The aim of this survey campaign was to assess the importance of each indicator from the point of view of the students as end-users. The results were collected and assessed. A ranking analysis has been conducted using an average proximity matrix and severity index statistical methods. A proximity matrix scaled 1-proximity for each data point was plotted to identify coherent clusters of respondents. In cluster analysis, a "proximity matrix" is a collection of similarity estimates between each of the items (criteria) in the responses set  (Dubes, 1999). For a set composed of k items, (k * (k À 1))/2 proximities must be acquired, such that each item is compared to every other at least once.
Using the proximity matrix also allows for discrimination among levels of severity. The average response time of the survey was 5-7 min. The survey was administered online. With the help of social media, a sampling balance was achieved; reaching more than 600 valid replies were registered in the Web-survey platform (see Appendix 1). The survey was conducted in accordance with the ethical 198 standards in the Declaration of Helsinki and the European Union General Data Protection Regulation (GDPR). The survey was launched between October 2017 and February 2018. The survey was written in French and targeted students in Wallonia, Belgium. Respondents were promised to receive a copy of the survey report if they are interested. The questionnaire responses were filtered, and incomplete responses were eliminated.
Results Six hundred ten students filled in the survey, forming almost half of the targeted respondents. Fifty-six percent of respondents were females, and 54% were males. The average age of respondents was 22 (61%) representing third-year bachelor students. Postgraduates formed 17% of the respondents, and undergraduates formed 83% in total. With the help of the SPSS program, Cronbach's alpha was calculated to test the reliability of the generated outcome. All alpha values were greater than 0. 7 for all criteria (reliability ⍺: 0. 75, 0. 78. 0. 71 for sub-indices A-C and 0. 74 for the aggregated index, respectively), indicating that all reliability coefficients are acceptable of the criteria included in the survey is high. In the following sections, we present the final classification criteria, ranking analysis results, developed tool, and the case study used to test the tool.
Confocal Microscopy Embryos were maintained from Day 0 with or without methylene blue as indicated. Agar-embedded 3 dpf embryos were imaged in an inverted Axio Observer LSM710 confocal microscope (Carl Zeiss, Oberkochen, Germany) to obtain spectral image stacks (x, y, λ). A PlanApo 10x/0. 45 NA objective and Zen2. 3 software (Carl Zeiss, Oberkochen, Germany) were used. The acquisition was configured to use the Lambda scanning mode with laser excitation at 458 nm and a 464-718 nm emission window. In this mode, images were scanned in x-y, and the emission from each point is spectrally dispersed so the entire spectrum is simultaneously gathered by a detection array with up to 32 channels of 9. 8 nm bandwidth each. The average intensity in ROIs drawn on the image λ stack was plotted along the wavelength axis to obtain the emission spectra.
We acquired fluorescence images of the heart in 72 hpf embryos expressing the ratiometric fluorescent biosensors TN-XXL, Twitch-1, Twitch-2b and Twitch-4 with a wide-field fluorescence microscope (DMIRE-2, Leica Microsystems, Wetzlar, Germany) equipped with a sCMOS camera (2048 × 2048 pixels, ORCA-Flash 4. 0, Hamamatsu Photonics, Hamamatsu, Japan), controlled by the software Aquacosmos 2. 6 (Hamamatsu Photonics, Hamamatsu, Japan). The image acquisition rate was 50 Hz (20 ms integration per image) during 5-10 s; some embryos were imaged at 100 Hz. To image at this speed, the donor (ECFP or mCerulean3) and acceptor (cpCitrine174 or cpVenusCD) FRET images were acquired simultaneously with an image splitter (W-View Gemini, Hamamatsu Photonics, Hamamatsu, Japan), which divided the camera field in two halves corresponding to donor and acceptor emission.
Data shown for each embryo represent the average of the cardiac cycles in 5 or 10 s of continuous recording (typically about 18 or 36 cardiac cycles, respectively), as indicated. Transmitted light images were analyzed manually in ImageJ  [49]. For the calculation of the HR by transmitted light, one ROI was placed just outside the edge of the ventricle during systole. The intensity changed whenever a diastole occurred because of relaxation of the heart wall; the HR was taken as the number of peaks per minute. To evaluate the contractility, the ventricular fractional area change was calculated from frames at end-diastole and end-systole (fully dilated and fully contracted ventricle, respectively)  [50]. The area of the external edge of the ventricle was measured because the internal edge showed less contrast.
Statistical Analysis Statistical analysis was done with Igor Pro 8 (WaveMetrics, Lake Oswego, OR, USA) and GraphPad Prism 6 (Graphpad Software, San Diego, CA, USA). All data in the figures are shown as the mean ± standard error of the mean (S. E. M. ). The statistical significance between two groups was determined using a paired or unpaired Student's t-test, as indicated. The correlation between the ratio change and fluorescence intensity was analyzed using a linear regression analysis of the transformed data with SPSS (IBM, Armonk, NY, USA). A p < 0. 05 was considered statistically significant (* p < 0. 05, ** p < 0. 01, *** p < 0. 001, **** p < 0. 0001). Supplementary Materials: Supplementary Materials can be found at http://www. mdpi. com/1422-0067/21/18/ 6610/s1.
Funding: This research was funded by the  Ministry of Science, Innovation and Universities, Spain  ( BFU2015-69874-R , co-funded by  EU FEDER-ERDF) , by  Consejería de Educación ,  Cultura y Deportes ,  Junta de Comunidades de Castilla-La Mancha  ( SBPLY/19/180501/000223 , co-funded by  EU FEDER-ERDF ) and grants for research groups from  University of Castilla-La Mancha (UCLM)  ( 2019-GRIN-27019  and  2020-GRIN-29186 , co-funded by  EU FEDER-ERDF ). J. S. -A. held a predoctoral fellowship from  UCLM.
The use of a bioinformatics pipeline as a tool to support diagnostic and theranostic decisions in the healthcare process requires the definition of detailed development workflow guidelines. Therefore, we implemented protocols that describe step-by-step all the command lines and actions that the developers have to follow. Our protocols capitalized on two powerful and widely used tools: git and GitLab. They address two use cases: a nominal mode to develop a new feature in the bioinformatics pipeline and a hotfix mode to correct a bug that occurred in the production environment. The protocols are available as a comprehensive documentation at https://biogitflow. readthedocs. io and the main concepts, steps and principles are presented in this report.
This must be integrated in a quality management process with standardized protocols approved for a diagnostic use. There is a wide ecosystem of tools (see  Perez-Riverol et al. , 2016; Riesch et al. , 2020; Zolkifli et al. , 2018)  that can support the implementation of such protocols. Among them, we capitalized on git for the version-control system as it became a very popular tool in the software engineering community and GitLab for the repository manager as it can be self-hosted. Both tools offer a large set of very powerful functionalities that can be used and combined in multiple ways thus increasing their usage complexity. It is thus mandatory to formalize through detailed guidelines how to use them on a daily basis for the development and deployment of the bioinformatics pipeline. Therefore, we implemented the set of protocols biogitflow  (Kamoun et al.
, 2020)  that describes step-by-step all the command lines and actions to be performed by the developers. These protocols are mainly dedicated to bioinformaticians and software developers who are involved in the development, deployment and maintenance of bioinformatics pipelines that support routine production. For example, core facilities (such as sequencing platforms) generate, with a very a high-throughput, many data that have to be processed and delivered on the fly to the end-users. Short time-to-delivery and continuity of service under any circumstances is a challenge that can be tackled by promoting these protocols to pave the way towards better industrialized processes for the software component in the context of production, in particular in healthcare. A comprehensive documentation available at https://biogitflow.