dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion and Outlook Photon sources based on XFELs, and especially the high-repetition rate ones such as in the European XFEL, offer incredible potential for serial femtosecond crystallography. This comes at the cost of complicated setups, which are also reflected in the data structures, as well as overwhelming data rates that need to be tackled by nonroutine data processing. A challenge that requires expert knowledge and/or iterative processing steps lies in the optimization of the parameters, such as a minimum signal-to-noise ratio, peak finder thresholds, et cetera. Technically, as these parameters are passed to the CrystFEL programs through a configuration file, iterative runs require a re-editing of the configuration, which can quickly become tedious. Currently, EXtra-Xwiz offers two ways to simplify this process: first, the software includes a grid search, as described in Section 3.
This solution is being deployed at the EuXFEL and integrated into EXtra-Xwiz. The same approach can also be used to tune the detector geometry representation in laboratory space. Indeed, the overall SFX workflow can greatly benefit from automating this task. At the moment, this is largely done manually. The operator is typically using graphical software for the visual centering and quadrant fitting of the powder diffraction rings, followed by iterations of indexamajig and geoptimizer  [67] , for maximizing the yield of frames that can be indexed. As a more long-term outlook, we strive to give users an end-to-end experience, that is, a pipeline covering all of the steps from detector images to an atomic structure built into the reconstructed electron density. For this purpose, downstream modules handling the program execution related to the tasks of crystallographic phasing, model building, and (preliminary but automated) structure refinement need to be included to the EXtra-Xwiz framework.
The integration of different SFX processing software, such as DIALS, as alternatives to CrystFEL would give users more options and allow a more objective, respectively comprehensive, data analysis. Such integration is planned, as it will be facilitated by the modular nature of EXtra-Xwiz. It should be pointed out that challenging problems, particularly with respect to de novo structures, will require user experience for decisions along the way, for instance, these include which phasing method to apply or which search model to use for molecular replacement. This means that the design of the workflows has to be well-thought, thereby allowing user intervention where required and a good balance of parameters with reasonable defaults versus expert input, thereby boiling the process down to a useful semiautomatic approach.
A sensitivity analysis is then conducted to account for uncertainty and variability in the model using Spearman's rank order of correlation coefficients. Based on the risk assessment framework, a search was carried out using the Web of Knowledge and Scopus to find data appropriate for each stage. Step 1 involves the calculation of A. flavus colonies found in soil as a result of previous crop debris. Step 2 involves the application of fungicide and the reduction of viable A. flavus colonies. Step 3 involves the dispersal of these spores onto nearby maize plants. Step 4 calculates the lag time, growth and diameter of A. flavus colonies based on temperature and water activity. Step 5 calculates the production of aflatoxin B 1 based on A. flavus diameter. Steps 1 to 5 are included in the preharvest stage.
Step 6 calculates the amount of aflatoxin B 1 consumed by bovines. Step 7 calculates the bio-transfer of aflatoxin B 1 to aflatoxin M 1 from feed to milk. Steps 6 and 7 are part of the livestock stage. Step 8 calculates the estimated daily intake of aflatoxin M 1 by human consumers. Finally, step 9 characterises the risk from the consumption of aflatoxin M 1 for the population of Ireland and France. A comparison was carried out between aflatoxin M 1 consumption from milk in Ireland and France. Both Irish and French scenarios were considered under 2 different climate change scenarios, and a baseline scenario was used by taking weather data from a 30-year average  for both countries. The effect of change in climate was considered by taking the potential changes in temperature and relative humidity, which could impact the growth of A.
Consumer stage Consumption data for France and Ireland were taken from their respective national dietary surveys. For Irish data, the Irish Universities Nutrition Alliance's (IUNA) National Adult Nutrition Survey (NANS) data report from 2008 to 2010 was used (denoted as Intake in Table  1 ) (Irish Universities Nutrition Alliance, 2011). The mean consumption of liquid whole milk in Ireland for adult men was 215 g day -1 , and the standard deviation was 214 g day -1. For adult women, the mean consumption of liquid whole milk was 116 g day -1 , and the standard deviation was 139 g day -1. The mean body weight (bw) for Irish men and women was 86. 2 and 70 kg, respectively, and the standard deviation was 15 and 13. 7 kg, respectively. France's liquid whole milk consumption data was obtained from the Third Individual and National Survey on Food Consumption (INCA3) (Agence nationale de sécurité sanitaire de l'alimentation del'environnement et du travail, 2017).
Climate data Climate data for both France and Ireland were taken from their respective meteorological websites, Met Éireann (Met Éireann, 2020) and Meteo France (Meteo France, 2020). The temperature (T) distributions for each of these countries were used based on historical data of 30 years. Hourly relative humidity data and saturated vapour pressure were collected from Met Éireann (2020) for the years 2011-2022. This was used to obtain vapour pressure deficit for 2 different scenarios based on the months March, April and May (MAM) and June, July and August (JJA). Two future potential temperature scenarios were considered in the model, scenario 2-4. 5 and scenario 5-8. 5, based on the shared socioeconomic pathways published in the sixth Intergovernmental Panel on Climate Change (2021) report.
However, as Italy is part of the European Union, it has to maintain the maximum permissible levels in milk and feed. The margin of exposure for different age groups calculated by  EFSA (2020)  for European countries was between 100,000 and 2564 for the mean lower bound exposure (0. 04-1. 98 ng kg -1 bw day -1 mean exposure to aflatoxin M 1 ), and between 33,333 and 642 for the mean upper bound exposure (0. 12-7. 88 ng kg -1 bw day -1 95th percentile dietary exposure to aflatoxin M 1 ) suggesting that certain populations in Europe might be at risk to hepatocellular cancer by dietary exposure to aflatoxin M 1. Farkas et al. (2022)  used a probabilistic approach to characterise the risk of aflatoxin M 1 exposure through milk and dairy products for different age groups in Hungary. The study used aflatoxin M 1 concentrations and dairy product consumption data from a survey conducted between 2018 and 2020.
Punctuality is a sensitive issue in large airports and hubs for passenger experience and for controlling operational costs. This paper presents a real and challenging problem of predicting and explaining flight off-block delays. We study the case of the international airport Paris Charles de Gaulle (Paris-CDG) starting from the specificities of this problem at Paris-CDG until the proposal of modelings then solutions and the analysis of the results on real data covering an entire year of activity. The proof of concept provided in this paper allows us to believe that the proposed approach could help improving the management of delays and reduce the impact of the resulting consequences.
One of the specificities of our work is that we focused on very fluctuating data due to the COVID-19 pandemic. The main contributions of the paper are the study of the existing situation at Paris-CDG, the needs, and then the identification of three main tasks real-time prediction of parking delays, forecasting, and explicability of predictions. Concerning the modeling, we have identified five categories of data for our problem: flight data, data on the progress of passenger processes (security, boarding, Etc. ), weather data, current delay status data, Etc. Finally, we have conducted an empirical study to perform the data, feature, and model selection, and we provide an overview of the results obtained.
In  (Yi et al. , 2021) , the authors have proposed several stacked approaches for the Boston Logan International Airport flight dataset from January to December 2019. We also find different studies on the impact of different factors on flight delays. For example, the authors of  (Wang et al. , 2003)  studied the impact of flight connections on delay. In  (Markovic et al. , 2008) , a statistical study on the impact of weather at Frankfurt airport is proposed. In (Yogita  Borse et al. , 2020) , the authors focused on weather data as the main feature to predict the delay class. In  (Esmaeilzadeh and Mokhtarimousavi, 2020) , a support vector machine (SVM) model is used. Based on 20 days, this latter study examines some causes of air traffic delays at the three major airports in New York City. In the study  (Cai et al.
Real-Time Off-Block Delay Prediction The problem considered here is the one of predicting at any time t, the off-block delay Y (expressed in minutes) that this flight will indeed have. These predictions are updated every 5 minutes until the flight leaves its stand. Indeed, for every slot (each slot lasts 5 minutes), we can acquire new relevant data from the Paris-CDG operational information system. These data can be used to update the predictions (this is valid for dynamic variables such as weather conditions, the progress of passenger processes, etc. ). Real-time offblock delay predictions are intended mainly to: • Draw managers' attention in real-time to flights likely to have significant delays and which may have cascading consequences. • Explain and identify actionable causes if necessary to resolve the situation.
Data Extraction and Preprocessing Paris-CDG's operational information system (called AOP for Airport Operation Plan) collects much information about each flight and its progress. For our prediction tasks, a new flight entry is created with the associated time stamp and updated data at each time slot. It is, therefore, possible to trace the status of a flight back to its departure. Therefore, for static characteristics, we extract them only once. For dynamic characteristics, such as delays of other flights, these are calculated variables that we perform with queries on past flights. For example, to compute the proportion of flights that have been delayed in the last w minutes, we need to review all flights involved in the w time window. The delay characteristics of previous and current flights (PCFDF) are computed after extraction with different w windows.
16 to 0. 23. Finally, we can note that the dynamic variables (in particular, the variable concerning the progression of the boarding) have a score in progression. This progression shows the importance of using dynamic flight data for real-time predictions. In order to validate the findings on the correlation scores obtained, we performed another empirical analysis by varying the set of variables used for delay prediction. We also varied the window w used to calculate the dynamic variables from the nearby history and the amount of history to use (twelve months, nine months, six months, or three months). We used an ensemble model of boosted regression trees regression trees called LightGbm. In the following, we denote by D m w the dataset with m the number of months used (m ∈ {3, 6, 9, 12}) and w the duration of the window used for the computation of the dynamic variables (w ∈ {10, 30, 60}).
V denote the set of variables used for the dataset (V ∈ {{BFF}, {BFF,WCF, OMF, PFF}, {BFF, PCFDF,WCF, OMF, PFF}}). For our study, we tested our configuration on 40 days (from August 12 to September 21, 2022). For each day, we trained the model until the day before the test day and evaluated it on the test day. Table  5  shows the optimal configurations. The errors and R2 score presented in this table are for the 40 days tested. The hyperparameters are noted as #Tree/#Leaves/LearningRate. Table  5  presents the results for the different configurations. The use of dynamic variables dynamic variables bring a real gain, reducing the error by half and significantly improving the R2 score. Thus the best configuration uses all of the history (12 months), with a time window of 60 minutes.
CONCLUSIONS Punctuality is a sensitive issue in major airports and hubs for the passenger experience. In this article, we have addressed the problem of predicting delays at the departure of parking lots at Paris-CDG airport, one of the largest airports in the world and the hub of the airline Air France. Our study started with analyzing the problem (its magnitude, form, causes, Etc. ) and the needs (real-time forecasting and prediction) at Paris-CDG. Based on this analysis, we proposed two types of feature categories that could be useful for delay prediction: static features used for the forecasting task and dynamic features (which can be updated in real-time) used for real-time delay prediction. The next step was to build a pipeline to extract the raw data we needed from the operational information sys-   tem of Paris-CDG.
This allowed us to build a dataset representing one year of authentic activity. We conducted an empirical study to select the characteristics and the data and then to select the models. One of the specificities of our work is that we worked with very fluctuating data due to the COVID-19 pandemic pandemic and its consequences in terms of air travel restrictions on several occasions, as well as other air traffic hazards. The results show that some delays can be predicted much better than the baseline model. This result can be significantly improved by systematically exploring other models and their best hyperparameters. In addition to the accuracy improvement, one of the crucial elements for our application will be the explicability of the predictions, in particular, the identification of explanations that can help in delay management.
The last step encompasses the morphogenetic processes that involves patterning, differentiation and growth of the reformed structure  [2, 6, 7]. Despite its crucial role in animal life cycles, the fundamental process of regeneration remains poorly understood, and elucidating its molecular mechanisms is still a lively question  [8, 9]. One way to approach is to identify genes that are activated during the main steps of this process. Transcriptomic data, notably staged-bulk RNA-seq datasets, have shown to be an efficient way to achieve that. Major advances in the regeneration field were accomplished due to transcriptomic studies. These include studied targeting long-lasting questions regarding (i) the extant of recapitulation of development during regeneration and its fidelity  [10] [11] [12] , (ii) the (re)patterning and integration of a newly (re)formed structure  [13, 14] , (iii) the variation of regenerative capabilities  [15, 16]  and (iv) the evolutionary history of regeneration  [2, 17].
At 3 dpa (stage 3), the blastema enlarges and extensive cell proliferation is observed. Between stages 2 and 3, the majority of the tissues and structures (i. e. muscles, segments, nervous system …) begin to reestablish. At stage 5 (5 dpa), the growth zone is functional and have started to produce a few segments, while still not morphologically visible. As such, at this stage, the regeneration process is finished and posterior growth has resumed  [42]  (Additional file 1). In terms of genomic and transcriptomic resources, there is currently no published Platynereis dumerilii genome, while some transcriptomic data has been essentially generated from embryonic and larval stages  [43] [44] [45] , as well as from brain tissue  [46]. Few scRNA-seq datasets from early embryos and larvae have also been generated  [47, 48].
This experimental strategy has the advantage of combining the strengths of both tools and fully exploit all our sequencing datasets (short and long reads) in depth. Moreover, merging transcriptomes from different assemblies and sources using EvidentialGene gives rise to better quality assemblies (e. g. measured in the number of full-length protein-coding transcripts) and single-tool transcriptome assemblies, as noted in plants and mammals  [50, 51]. We assembled three intermediate transcriptomes, using various combination of series 1 and/or series 2 data, as well as different assembly tools (see Methods for more details, Additional file 3). We named these intermediate assemblies Hybrid 1, Hybrid 2 & Blastema (Additional file 3). We merged these three transcriptomes, along with two additional transcriptomes available from the literature, one encompassing seven early embryonic stages (referred to as the Embryonic transcriptome,  [45] ), and the other encompassing the head of male and female worms during sexual maturation (referred to as the Head transcriptome,  [46] ) (see Additional files 2 and 3).
Although the number of transcripts in our Reference transcriptome is average, it has the highest mean and median transcript lengths (1 890 bp and 1 256 bp respectively), the highest number of transcripts longer than 1 000 bp, and the second highest number of transcripts longer than 10 000 bp (Fig. 1A , Additional file 4), which indicates gain of assembly quality with this Reference transcriptome as compared to formerly available transcriptomes. This also highlights that, as expected, the inclusion of long read data allowed to obtain of very large -potentially complete -transcripts, in comparison to Illumina reads (even 150 bp). Regarding the assemblers we tested, our analyses confirmed that rnaSPAdes is much more powerful than Trinity to integrate data of different types, i. e. Illumina short read and ONT (Oxford Nanopore Technologies) long-read, as expected based on previous studies  [49].
The objective here is to have a Reference transcriptome that can be used in different biological contexts, from embryonic development to regeneration. Hence, we mapped RNA-seq reads from the "Blastema", "Head", and "Embryonic" datasets (Additional file 2) onto the six transcriptomes. Globally, the "Head" and "Embryonic" transcriptomes generally have the lowest mean percentage of mapped reads amongst the RNA-seq datasets that we investigated: 75% and 80% compared to at least 91% for other transcriptomes in the Blastema series, 57% and 72% compared to at least 75% for the Head series (Fig. 1B , Additional file 5). The Embryonic transcriptome, however, has one of the highest mapping percentages for the Embryonic series (91%, (Fig. 1B , Additional file 5), which can be explained by the fact that the Embryonic transcriptome was reconstructed from the RNA-seq reads of the Embryonic series.
1  Quality control metrics for Platynereis dumerilii transcriptomes A) Total number of transcripts (left), mean transcript length (middle left), number of transcripts with length above 1000bp (middle right) and above 10 000 bp (right) are shown. All transcriptomes are represented, including our intermediate assemblies (Hybrid 1, Hybrid 2 and Blastema), the two from the literature (Head and Embryonic), and our Reference assembly. B Mean alignment rate (percentage of reads mapped) for each transcriptome assembly. Reads from the Blastema, the Embryonic and the Head datasets (see Additional file 2) were mapped on the six transcriptomes with bowtie2  [54]. C BUSCO completeness metrics for the Embryonic, the Head, and our Reference transcriptomes. Complete single-copy and complete duplicated were merged because of the redundancy that is normally occurring in de novo transcriptome assemblies.
D Swiss-Prot BLASTX results for each transcriptome assembly. Total bar height represents total number of transcripts with a significant BLASTX hit (e-value cutoff 10 -5 ), colored bars represent the number of transcripts having the indicated coverage with a Swiss-Prot sequence. All BLASTX were done on the non-redundant Swiss-Prot database (See figure on next page. ) achieving with our Reference transcriptome. As such, even though it harbors lower mapping percentages than the tissue/stage-specific assemblies, we argue that our Reference transcriptome represents the right balance between biological information and mapability. We then determined the completeness of the transcriptomes using BUSCO. Using either the Eukaryota, Metazoa or Arthropoda conserved single-copy orthologs gene sets, the Reference transcriptome has the highest BUSCO scores (from 98.
2 to 100%) compared to other assemblies (Fig. 1C  and Additional file 6). We also looked at how well assemblies could recapitulate known P. dumerilii transcripts that are present on the NCBI database at the time of our analyses (n = 13,554). The Reference transcriptome has the highest recall rate for known transcripts, with more than 99% of P. dumerilii transcripts having a significant BLAST hit, compared to 93% for the Head and almost 98% for the Embryonic transcriptome (Additional file 6). This clearly indicates the high consistency between our Reference transcriptome assembly and the already known Platynereis genes. We finally analyzed how well our transcripts recapitulated known proteins using BLAST analyses on Swiss-Prot database (Fig. 1D  and Additional file 6). Our Reference transcriptome has the highest percentage of transcript with at least one significant hit (e-value cutoff of 10 -5 ) with 54% of transcripts, against 14% and 37% of transcripts for the Embryonic and Head transcriptomes, respectively, and 32%, 33% and 23% for the Blastema, Hybrid 1 and Hybrid 2 transcriptomes, respectively.
Functional annotation of the reference transcriptome We finally annotated the Reference transcriptome using both Trinotate (Additional files 7 and 8), or individual BLASTX searches on six specific species: four deuterostomes (Homo sapiens, Mus musculus, Danio rerio, and Saccoglossus kowalevski) and two protostomes (Drosophila melanogaster, and Aplysia californica) (Additional files 8 and 9). Trinotate results indicate that at least 57% of P. dumerilii transcripts have a significant BLASTX hit. Furthermore, we could find Pfam domains on 54% of all our transcripts and assign more than 50% of our transcripts to a KEGG pathway. While the percentage of KEGG-annotated transcripts is higher than reported in the "Embryonic" transcriptome, the percentage of transcripts with at least one Pfam domain is lower.
Moreover, while around 80% of O. fusiformis transcripts had a significant hit (e-value cutoff value of 10 -3 ) in a non-redundant database of annelid genes  [55] , 68% of P. dumerilii from our Reference transcriptome had such a significant hit. Although these numbers indicate that our Reference transcriptome assembly does not attain the quality of a reference genome, the (relatively) high figures we obtain with these BLAST analyses should be taken as an indication of the quality of our transcriptome assembly. Taken together, these results show that our de novo Reference transcriptome is a high-quality transcriptome that probably represents the majority of genes that are transcribed during developmental events, with a high fraction of complete gene models and is thus well suited for the study of various biological processes of P.