dataesr/scientific-paragraphs-categorization huggingface dataset:
The complete study duration for participants is four and a half months (19 weeks; see Fig. 1  for an overview of the study). Participants in the control group have measurement visits, but no intervention visits before benefiting from the ALAPAGE program. The ALAPAGE study design was tested in a pilot study between September and February 2022 and was based on two intervention and one control workshops. This pilot study led us to make several modifications to the intervention (e. g. , the increase of the sessions' duration from 2 to 2. 5 hours; the revision of the training content/duration that professionals undertook before the intervention; the improvements to the intervention tools), the data collection procedures (e. g. , the addition/removal of items in self-administered questionnaires) and research documentation (e.
g. , are able to give free and informed consent and to complete self-administered questionnaires) and who meet the regulatory requirements (health insurance). (  2 ) Recruited using an active recruitment strategy, which was previously designed and pilot-tested by our research team, that targets hard-to-reach people  [46]. In sum, this strategy aims to increase the participation of socioeconomically disadvantaged people and/or socially isolated older people in health-prevention programs on diet and physical activity by the following four steps: (i) identification in the retirement fund database of targeted older people; (ii) invitation letter by mail; (iii) telephone call by a social worker; (iv) home visit by the same social worker. The inclusion criteria are: 60-80 years old who live at home in the municipality where the prevention workshop will take place, receive allocation for economically deprived and/ or socially isolated older people, have a telephone number recorded in the retirement fund database and be able to read and write in French.
Effectiveness evaluation Primary and secondary outcomes, with few exceptions, are assessed before the intervention ("T0"), at 6 weeks ("T1" corresponding to the end of ALAPAGE program's session period) and then 3 months later ("T2") (see Table  2  for an overview of outcomes and data time points). Primary outcomes Dietary diversity is assessed using the Diversity ALAPAGE Score (DAS). Based on dietary diversity scores developed abroad  [50] [51] [52] , we developed the DAS using individuals' consumption habits of 20 food categories (see Additional file 3 for more details). Individuals must complete two 24-hour diet recalls (for foods that they usually eat every day, such as fruits, vegetables, meat excluding poultry and sweetened products) and one Food Frequency Questionnaire (FFQ) (for foods that they consume less frequently, such as eggs, legumes, nuts, fatty fish, see Additional file 4).
Each consumption earns positive or negative points according to the nutritional quality of its food category. The DAS for one individual is calculated by adding all attributed points. A higher DAS means a greater diversity in diet. We found that DAS was positively associated with the quality of diet among a representative sample of French people age 60 years or older  [53] , and that a low DAS was associated with an increased death risk among a cohort of older French people who were followed over 15 years  [54]. Lower-limb muscle strength is assessed using the 30s Chair Stand Test from the Senior Fitness Test battery (adapted in French)  [55]. Participants repeatedly stand up from and sit down on a chair for 30 seconds and an APA professional records the number of complete stands. Secondary outcomes Consumption frequencies of main food groups and water/hot drinks are assessed using data from two 24-hour diet recalls and one FFQ.
Expected and non-expected serious adverse events and other unintended effects of the intervention (i. e. , those that require hospitalization, result in a significant or long-lasting disease/disability, or death) are collected and immediately reported to the research team by the sessions' dieticians and APA professionals. Prior to the commencement of the study, dieticians and APA professionals participate in training sessions that are led by research team members (including by one person who is certified in Good Clinical Practices [GCP]) and includes (i) a two-hour video conference that presents the study and its data circuit; (ii) a half-day session on research issues (e. g. , clinical practices, information, consent, notification of adverse events); and a further one and a half day session on the study's content and visits (e.
Data management The participants are each given a unique identification code (without any personal information that could allow for their identification). During the study period, all of the collected data is stored in a locked cabinet at the local organizations, according to a procedure compliant with GCP guidelines. When the study is complete, the collected data will be securely transmitted to the research team's logistic department, who will create the databases. The collected data will be stored according to standards for archiving research materials. Leaders of the research team who are involved in the ALAPAGE study and who have signed a consortium agreement will have access to the final study dataset.
Data analysis All statistical analyses will be performed using SAS 9. 4 (SAS Institute Inc. , Cary, NC, USA). A α of 0. 05 will be used to determine statistical significance. First, descriptive statistics (mean ± standard deviation, median, and frequency distribution) will be carried out to describe baseline characteristics (sociodemographic, life conditions, primary and secondary outcomes) of participants in both groups. To compare the baseline characteristics (age, gender, life conditions) between the two groups, a one-way ANOVA or a Kruskal-Wallis test will be used for continuous variables and a Chi-square or a Fisher's exact test will be used for categorical variables. As part of the effectiveness evaluation, linear or logistic (depending on the nature of the dependent variable) mixed models will be carried out to study the impact of the intervention on primary and secondary outcomes, while taking into account the repeated nature of the data and the intra-cluster correlation.
Time, group and their interaction will be defined as fixed factors (see Additional file 7 for more details). If imbalances occur between the groups, the baseline values will be added as covariates in the models. Missing data will be inspected, and, if appropriate, will be handled using multiple imputation. As part of the process evaluation, we plan to perform mediation analyses to verify whether beliefs, attitudes, and intention are pathways by which the intervention impacts physical activity. Furthermore, we plan to perform moderating analyses to test whether the effect of the intervention varies according to some characteristics (in particular, type of recruitment, antecedents of physical activity or history of falls, and baseline dietary diversity). As part of the process evaluation, qualitative data from participant interviews of the intervention group will be transcribed and analyzed using a data-driven inductive approach to explore how the intervention may have resulted in behavioral changes.
Acknowledgements The authors would like to thank all of the dieticians and adapted physical activity professionals, who contributed to the design of the ALAPAGE program (as part of the participation process); the staff from local organizations who volunteered to participate in the ALAPAGE study; the professionals and older people who accepted to participate in the trial's pilot study; the social workers and the management team from SudEval (the partner responsible for the recruitment of hard-to-reach older people); as well as all of the members of the steering and scientific committees. The authors also thanks Mathieu Bertrand, Elisa Monticelli, Romain Paisant, Rosalie Prat, Julienne Sarthou, and Cindy Voix who contributed to the development of the ALAPAGE Study during their internship. †The ALAPAGE Study Group includes the authors of the present manuscript and: Valérie Arquier 10 , Guillaume Briclot 10 , Rachel Chamla 11 , Florence Cousson-Gélie 12,13 , Sarah Danthony 8 , Karin Delrieu 14 , Julie Dessirier 14 , Catherine Féart 3 , Christine Fusinati 11 , Rozenn Gazan 15 , Mélissa Gibert 16 , Valérie Lamiraud 14 , Matthieu Maillot 15 , Dolorès Nadal 16 , Christelle Trotta 10 , Eric O.
). • Matching pairs with transformer models: Transformers (  [1] ) applied on raw text should outperform the previous approaches. However, due to their limitations in processing long documents, the use of sentence embedding seems to be a good option (see  [2] ). Another one is to summarize the document, see for instance  [3]  and  [4]  for some applications to legal documents. This significantly reduces the length of the text and eases comparisons. Finally, the use of efficient versions of self-attention (i. e. local attention) makes it possible to handle long documents efficiently while ensuring low memory usage. The aim of this paper is threefold: 1. Extract metadata that can be used to link court decisions. 2. Build a dataset of pairs of court decisions (that match and do not match) in order to further train some machine learning and transformers models.
Dataset creation Both corpora being available in a version deprived of metadata, a NER model is used to extract such metadata in the first place. Most of these are located in the header or in the upper part of the text body, when no structured header is present. The following labels are created: • Number ('numéro de répertoire général') • Date of judgment • Jurisdiction of judgment • Party (claimant or defendant) Two datasets are built, one of 638 appellate decisions, the other made of 1496 judgments from several areas of law, not only in the commercial area. They are manually an-notated with these labels. Each decision may be annotated with several claimants and/or several defendants. The data labelling is done on the Kairntech platform  [13]  and statistics are reported in Table  1.
Post-processing and normalization Since NER is an extractive and not a generative task, some labels require prior normalization. As dates can be written in either literal or numeric form, a small BART model  [14]  is trained to convert dates to yyyy-mm-dd format. For this purpose a dataset is artificially created mixing numerical format, literal format, punctuation, typos and noise. Thousand of examples are generated, i. e: • Original string: "18 Juin 2010" • Noisy string: "e le 18 jiun, 2O1O. " • Label: "2010-06-18" As the NER task also gives the location of the extracted sequence in the document, the Seq2Seq generative task is performed on an extended segment of a few words before and after it to correct some truncated predictions. This normalization allows to classify documents by date and to extract the subset of 13,000 court of appeal decisions that are potential candidates for the linking process with the Paris Commercial Court.
Matching pairs from meta-data The manual rule-based program described above is used to build a dataset. Since document pairs are now known, the production of counter-examples is relatively simple and is done by randomly matching appellate with first-instance decisions. The task is defined as a binary classification task on tabular data made of two times 2770 rows and 5 columns (features) represented as strings: • Date of appellate decision • Date of judgment found in the appellate decision • Jurisdiction of judgment found in the appellate decision • Date of judgment found in the first-instance decision • Jurisdiction of judgment found in the first-instance decision. A binary classification task is then defined by concatenating the features of the document pairs. This task is then solved using standard machine learning algorithms such as SVM, logistic regression, multilayer perceptron (MLP) and decision-tree methods.
Matching pairs of documents with transformers The classification task presented above can be adapted to a contemporary Transformerbased approach. Contrary to the experiments presented so far, these models do not need to define a set of features but are applied directly on the raw document if a sufficiently large annotated dataset is available. This has several advantages: • It is not necessary to define manual rules • The model will determine itself the necessary and discriminating tokens • Its generalization is usually better and more robust given the high number of parameters and the pretraining process. Since the proposed task is actually a similarity task, a Siamese neural network  [15, 16, 17, 18]  approach is preferred, comparing the sentence embedding of the documents. A siamese network works on two parallel neural networks sharing their weights but working on different inputs.
It allows to obtain a unique and static representation with a fixed length to ease comparisons. The architecture is presented in Fig  1. The general architecture is similar to the one proposed by SBERT  [19]  but the underlying model is a CamemBERT  [20]  model warm-started from the official checkpoint and fine-tuned on 30Gb of legal data (MLM). However, the processing of legal documents imposes an additional constraint since transformer based models are for most of them trained for 512 tokens sequences and they cannot process long sequences due the quadratic complexity of self-attention. To measure which part of a decision is likely discriminating for the similarity task, four experiments are proposed on different parts of the document: • First 512 tokens • Last 512 tokens • Full document (up to 16384 tokens) • Summarized document (up to 512 tokens) The data extracted by NER and used for the construction of the dataset are mostly concentrated in the first part of the document, so we can assume that the first 512 tokens are sufficient to reliably constitute the pairs.
Since the decisions can be long, especially for the court of appeal, the last 512 tokens may not contain any discriminating information, lower performances are to be expected. In order to process the documents without truncating them, we rely on a conversion script to extrapolate from an existing model. 2 he fourth experiment based on summarization is particular since it is necessary to develop a summarization model alongside. This process is done in two steps. First, a dataset of 70K pairs of decisions/summaries is extracted from Légifrance 3  and focused on the Cour de Cassation and the Conseil d'Etat (summaries are available only from supreme courts). A BARThez model  [14, 21]  (Seq2Seq) is warm-started and fine tuned on the text in-filling and de-noising tasks on the 30Gb corpus of legal data.
Conclusion We have presented a methodology to reconstitute the judicial path of a case. Through NER and simple predefined rules, a dataset of document pairs was built, allowing the task to be solved using machine and deep learning. Two methods have been proposed, one leveraging metadata, the other one focusing directly on the raw document. The experiments carried out have shown that the use of transformer based models, trained on the first part of the documents (512 first tokens), achieve very high performance and commit only few errors compared to the other methods presented and related to metadata. In practice, the use of Siamese architecture also has advantages in production. Because each decision can be represented by a pre-computed fixed length vector through sentence embedding, the comparison of a document against a large number of candidates can be done efficiently.
Development of high-throughput sequencing enabled the detection of several genetic alterations in a single sequencing run and has the potential to overcome several of those limitations. However, classical mappers, such as Bowtie2 or BWA, cannot directly identify structural variants such as tandem duplications. Thus initially, the data produced were analysed using pindel, a general-purpose algorithm that detects and quantifies indels and structural variants  [18]. Although using a general-purpose software can be appealing, it may be underoptimised to address the specific problem of identifying and quantifying tandem duplications, especially FLT3-ITD. Thus, many methods were specifically developed to detect FLT3-ITD in high-throughput sequencing data  [19]. Yuan and colleagues distinguished the assembly-based methods from the alignment-based ones, and demonstrated better accuracy of alignment-based methods.
Implementation and benchmarking FiLT3r is implemented in C++ using the GATB library  [26]  and is freely distributed gitlab. univ-lille. fr/ filt3r/ filt3r. We compared FiLT3r with state-of-the-art approaches. We made a preliminary assessment on our cohort with several other software detecting FLT3-ITD-ext, such as pindel  [18] , ITDSeek  [27] , ScanITD  [28] , getITD  [29]  and Genomon ITDetector  [30]  (see Additional file 1: Table  1 ). However, with the later publication of  [19]  we preferred referring to their independent assessment and chose to use in our paper the best tool they assessed: FLT3-ITD-ext  [31]. We also added km  [21]  as it was not evaluated in Yuan et al's benchmark and showed good performances in our preliminary assessment. km is an alignment-free approach already presented in the introduction.
Benchmarking data We assessed the three software packages on high-throughput sequencing data of 185 patients aged from 18 to 60 years old, diagnosed with de novo or secondary AML at the haematology laboratory of the University Hospital of Lille. This population was composed of 114 FLT3-ITD positive patients and 71 FLT3-ITD negative patients, according to the gold-standard fragment analysis  [33]. The study was conducted according to the Declaration of Helsinki and was approved (nos. CSTMT289) by the Human Research Committee of Lille and the institutional review board of the Lille University Hospital Tumor Bank (certification NF 96900-2014/65453-1). 1 For a discussion on the choice of k, see the Additional files 1-15, section Choosing the parameters. Exons 14-15 of FLT3 were sequenced in all patients using the following methodology: the library was prepared by a capture method, targeting 62 genes with SureSelect-QXT (AGILENT ® ) according to the manufacturer's protocol.
Samples were sequenced with NextSeq Illumina ® (2 × 121 bp). The region of interest was sequenced with a mean depth of 2116x (range: 421. 32 -4538. 84). The base calling was performed with bcl2fastq2 (v:2. 20. 0) and fastq were trimmed with fastp (v:0. 20. 0). The number of paired-end reads per sample ranged from 1. 8 million to 35 million (median: 4. 8 million). The sequences were deposited on SRA (see Additional file 3). The quantification of the expected ITDs were obtained using the reference method (by capillary electrophoresis) explained in the introduction. To assess how the three software behaves on datasets with very different characteristics, we used public RNA-seq data. RNA-seq is gaining importance in clinical settings and RNA-seq data differs widely from capture sequencing in terms of quantity of data, variation of expression and random starting positions of the reads.
We launched the three software on data from CCLE (Cancel Cell Line Encyclopedia)  [34]. We randomly selected 76 samples from haematopoeitic and lymphoid tissues and 76 samples from lung tissues (list of accessions in Additional file 10). It is not known which samples should contain FLT3-ITD. However, from a biological point of view, we do not expect to detect any FLT3-ITD in lung tissues while we could expect some in haematopoeitic and lymphoid tissues. This dataset will help assess how the three software scale on massive datasets (from 112,328 reads to 1,364,951,510 reads, median: 72. 9M) with an average read length ranging from 68nt to 150nt (median: 101nt). Finally, to check that our results holds on controlled data, we simulated ITDs at different ratios with different qualities.
Capture sequencing of 185 AML patients The software results on HTS data were compared to DNA fragment analysis, the goldstandard method. Any duplication found by a software, at whatever abundance, and identified with the reference method was considered as a true positive. Any duplication found by a software with an allelic ratio (AR) above. 01 and that was not identified by the reference method was for now considered as a false positive. After analysing the results in details, we will change this definition of false positivity as HTS appear to be more sensitive to detect lowly expressed ITDs. Raw results are shown in Table  1  (a) (detailed results for each duplication are shown in Additional file 1). FiLT3r and FLT3-ITD-ext showed similar performances. FiLT3r was slightly better in terms of true positives, but at the apparent cost of more false positives.
km-JF is the time taken by Jellyfish (preliminary step required for km), km (0. 01) is the time taken by km with the corresponding threshold FLT3-ITD-Ext can be very time-consuming, taking several hours, while FiLT3r never took more than 10 minutes. The progression margin for FiLT3r is quite low as gunzip on the same files took about a third of the time took by FiLT3r (see Additional file 1: Fig. 2 ). For FiLT3r it appeared that using a Bloom filter dramatically speeded up the software as deactivating the Bloom filter makes FiLT3r one order of magnitude slower (see Additional file 1: Fig. 3 ). Memory consumption was very limited in FiLT3r as we stored a Bloom filter and a hash table of the reference sequence. We also stored reads that match the reference sequence in main memory, but there were generally just a few thousands of them.
RNA-Seq samples from CCLE As expected none of the software detected a FLT3-ITD among the 76 lung samples. This tends to confirm the three software have a very low false positive rate. However on 10 samples (all with more than 1 billion reads), FLT3-ITD-ext had to be killed before the results were output as it took too much time (more than 24 h) or too much memory (more than 100 GB). On the 76 haematological samples, some FLT3-ITD were detected by the three software. At the same 0. 01 threshold as for the capture sequencing, FiLT3r detected 3 ITDs that were also detected by km, although one of them was only detected by km with -p 0. 001 not with -p 0. 01. FLT3-ITD-Ext detected the remaining two. Fig. 6  Memory consumption of FiLT3r, km and FLT3-ITD-Ext on the 185 samples analysed. km-JF is the memory taken by Jellyfish (preliminary step required for km), km (0.
01) is the memory taken by km with the corresponding threshold km additionally detected a 60nt ITD in SRR8657348 that was not detected neither by FiLT3r nor FLT3-ITD-Ext. However this 60nt duplication seemed to be an artifact from a 30nt duplication that was actually detected by the three software. km seemed to detect a duplication of the duplication, however we found no read with evidence of such an event. FLT3-ITD-ext detected three ITDs above the. 01 threshold, two of which were not detected by FiLT3r or km: a 108nt ITD in SRR8615750, and a 105nt ITD in SRR8615696. The third ITD (a 78nt duplication in SRR8615696) was also detected by FiLT3r but at a much lower AR (. 0007 instead of. 02). In this sample we found evidence of only two reads containing the ITD breakpoint, casting doubt on the.
Simulated data We simulated FLT3-ITDs using itdsim which is part of ITDseek  [27]. Then the ITDs were sequenced in silico using art with the MiSeq v3 profile of errors  [35]. The detailed results are presented in the Additional files 1-15. In short, they confirmed the results obtained on real data. We also noticed that, the more sequencing errors there are, the more false positives FiLT3r has. However, those false positives are always quantified at ratio below. 001. Contrarily to km or FLT3-ITD-ext, FiLT3r did not miss any ITD on the datasets with a normal coverage. FiLT3r also provided the best trade-off between quantification and F1 score.