dataesr/scientific-paragraphs-categorization huggingface dataset:
Functional Pathways of the Microbiome We performed a Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) to predict functional pathway abundance related to PGdiets. Then, multiple groups comparison was computed using the STAMP software and the ANOVA statistical test. Out of 137 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, 2 functional pathways were significantly abundant in the study (p-value < 0. 05; Figure  5 ). The NC group showed enriched carbon fixation, whereas PG-diets showed enrichment in selenocompound metabolism.
Functional Pathways of the Microbiome We performed a Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) to predict functional pathway abundance related to PGdiets. Then, multiple groups comparison was computed using the STAMP software and the ANOVA statistical test. Out of 137 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, 2 functional pathways were significantly abundant in the study (pvalue < 0. 05; Figure  5 ). The NC group showed enriched carbon fixation, whereas PG-diets showed enrichment in selenocompound metabolism.
Raw Data Processing and Taxonomic Analysis Pre-processed reads were imported and analyzed using QIIME2 version 2020. 02  [37]. We used the DADA2 software package implemented in QIIME2 to denoise and correct Illumina sequenced FASTA2Q files by removing chimera sequences using the "consensus" method  [38]. Based on the quality plot of the DADA2, reads were trimmed using the following parameters: -p-trunc-len-f = 300; -p-trunc-len-r = 240; -p-trim-left-f = 6; and -p-trim-left-r = 6. For taxonomy assignment, a naïve Bayes classifier was trained on a GreenGenes 97% (version 13. 8) operational taxonomic unit (OTU) database with reference sequences trimmed to the V3-V4 region using QIIME2 plugin feature-classifier  [39].
Functional Profiles of Gut Microbiome Phylogenetic Investigation of Communities by Reconstruction of Unobserved States2 (PICRUSt2) was used to predict Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology (KO) genes from 16S rRNA data  [49]. The tree generated in PICRUSt2 with the maximum nearest-sequenced taxon index (NSTI) cutoff of 2 was complemented with the feature table resulted from QIIME2 plugin dada2 for hidden-state prediction (hsp) using the maximum-parsimony method  [50]. Followed by the prediction of KO genes, the KEGG pathways were then mapped to the KO genes using the PICRUSt2 package. Afterward, multiple group comparison was computed using the Analysis of Variance (ANOVA) statistical test (p-value < 0. 05) in the STAMP software package to carry out significant pathways  [51].
Statistical analyses We ran Levene's test to check the homogeneity of variance assumption (center is equal to the mean). We ran a two-way ANOVA model for the full dataset on eroded soil mass, eroded TOC, PyC recovery (runoff and splash sediment, runoff and splash floating particles) and subsequently a two-way ANOVA model per soil type (Cambisol and Luvisol) for the same datasets. For the soil core data, we used a two-way ANOVA model for each slope position (upslope, midslope and downslope) and depth increment (0-1, 1-3, 3-10 cm) for the full dataset and subsequently for the individual soil types. We used the Shapiro-Wilk tests on the ANOVA residuals to check the normality assumption and Fisher's least significant difference (LSD) post-hoc tests (alpha = 0. 05, p. adj = Bonferroni) on the two elements of the four drivers.
Acknowledgements. The authors acknowledge the support and ability to use the facilities at the  Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) , especially  Manfred Staehli  and  Werner Gerber. We further thank  Thomas Keller  ( University of Zurich ) for technical assistance. Financial support. This research has been supported by the  Swiss National Science Foundation  (grant no. 200021_178768 ). Review statement. This paper was edited by  Yakov Kuzyakov  and reviewed by  Diana Vieira  and one anonymous referee.
► Clinical pulmonary infection score evolution after randomisation (score calculated daily based on available data from routine monitoring from randomisation to extubation or day 28, whichever occurs first  [32] [33] [34]  ). ► Incidence of ventilator-associated events  35   ► Number of days spent in the ICU and in the hospital after randomisation. ► ICU, hospital, day-28 and day-90 mortality. ► Incidence of antibiotic-resistant bacteria isolated on routine clinical and hygiene samples from randomisation to day 28. All bacteriological samples collected after randomisation will be recorded. The incidence of patients with samples growing bacteria resistant to amikacin otherwise naturally sensitive to amikacin and the incidence of multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria will be compared between groups.
36 ► Incidence of bacteria producing extended spectrum beta-lactamase or high level derepressed cephalosporinase, incidence of Enterococcus spp resistant to vancomycin on rectal samples at ICU discharge compared with randomisation in both groups. ► Incidence of AKI from randomisation to day 28. AKI will be defined according to the KDIGO classification. 26  priori defined exploratory outcomes: ► VAP suspicion by investigators filed to the adjudication committee from randomisation to day 28. ► VAP incidence density according to risk exposure from randomisation to day 28. Incidence density will Definite VAP diagnosis will be made a posteriori by an adjudication committee based on possible VAP case report form comprising clinical, microbiological data and original chest X-rays. *In patients suffering from the ARDS, chest X-ray interpretation for a new infiltrate may be difficult.
37 Taxonomic levels will be determined using the RDP classifier with a confidence value of 0. 5 considered as a positive identification. 38  Sputum and serum amikacin pharmacokinetics will be reported separately of the principal analysis. Pharmacokinetic analysis will be carried out over the full duration of inhaled administration. 1-3 tracheal aspirates and 1-3 serum samples will be drawn per patient in the subsample of patients of centres participating to the pharmacokinetic study. To preserve blinding, samples will equally be taken in patients receiving amikacin or control. Samples will be frozen and analysed centrally at study end, after database locking, using a liquid chromatography combined with tandem mass spectrometry assay given the low systemic concentration to be expected. A total of 700 samples will be analysed, thus depending on recruitment among centres participating to the pharmacokinetic study, between 2 and 6 concentration measurements will be available for a subsample of 100-300 patients.
Data collection, management and monitoring Nebulisation visits: nebulisation implementation and tolerance data will be recorded (table  2 ): ventilator settings, duration of nebulisation and occurrence of side effects. A check list will be provided. Among centres and patients participating in the pharmacokinetic and microbiota prespecified ancillary analysis, tracheal aspirate, rectal and oral swabs as well as serum samples will be taken. Microbiota samples (oral and rectal swab, tracheal aspirate) will be taken before the first study drug delivery and after the third study drug delivery. Daily visits until extubation or day 28 whichever occurs first Those visits will be performed every day after inclusion, including on days of nebulisation as long as patients remain invasively ventilated.
Predetermined ancillary analyses ► Microbiota analysis: bacterial taxonomic levels will be determined and clustering analysis will be carried out using UniFrac distance based principal coordinates analysis. 42 Clustering analyses will be carried out using operational taxonomic units relative abundance based on read numbers as well as UniFrac distancebased principal coordinates analysis. Alpha diversity in the respiratory and digestive microbiota datasets will be calculated using both Shannon and phylogenetic diversity metrics. ► Pharmacokinetic analysis: tracheal aspirates and plasma concentrations (randomly distributed over the three sampling time frames: 0-6 hours, 6-12 hours Open access  Open access and 12-24 hours) will be analysed using a population pharmacokinetic approach with Nonmem7 (ICON Development solutions, Hanover, Maryland, USA), to determine amikacin exposure within aspirates and to investigate the relationships between its pharmacokinetic parameters and covariates.
The population model will be adapted from previously published models. 43 If appropriate pharmacokinetic parameters values estimated during the study will then be used for Monte Carlo simulations with the Berkeley Madonna software (V. 8. 3. 18. , University of California, Berkeley, California, USA) to estimate the probably of reaching the target for the relevant pharmacokineticpharmacodynamic index for efficacy and safety. Different nebulisation optimisation factors (circuit, humidification, ventilator settings, sedation) will be evaluated. ► Respiratory mechanics will be analysed comparing compliance and resistance of the respiratory system changes before and after nebulisation in study groups taking into account the paired nature of the data.
Dissemination timeline The duration of patient enrolment expected is 4 years, starting in July 2017. ► End of 2015: national grant award. ► 2016: ethics and competent authority approval. ► 2017-2021: inclusion of patients; 805 out of 850 patients included the 14 December 2020. ► 2021: data monitoring and queries to investigators; blind review to determine protocol violation, cleaning and closure of the database. ► 2022: data analysis, writing of the manuscript and submission for publication.
Acknowledgements The authors sincerely thank  Grégory Reychler ,  David Grimaldi ,  Kada Klouche  for serving in the data safety monitoring board. Contributors SE, ET, J-CL, QL, FB and PL, in collaboration with the scientific board of the  CRICS-Triggersep network and the study steering committee designed the study. ET provided substantial contribution to the conception and design of the study and wrote the statistical analysis plan and estimated the sample size with SE and J-CL. LV, DLP, MC and RR carried out in vitro analysis of the nebulisation set up and pharmacy logistical development of active treatment and placebo. SE drafted the manuscript. FB, FM,  J-PQ ,  J-EH , MLa, DR, PS, DS, AV, BV, SL, QL, GB, MF, CD-F, GP, M-AN, HM, PA, GM, JH, TB,  J-CL  and SE include patients. MLe coordinated project management, study setup and quality control.
Funding The study was funded by the ' Programme Hospitalier de Recherche Clinique National 2015 ' of the  French Ministry of Health  (grant number:  PHRC-15-260 ) and ' Association pour la promotion à Tours de la Réanimation Médicale ' (grant number: not applicable). The study primary sponsor is the  University Hospital of Tours , Tours, France (2,  Bd Tonnellé. 37044 Tours cedex. France). consultancies from  Aerogen, La Diffusion Technique Française ; unrestricted research grants from  Fisher & Paykel Healthcare ,  Aerogen Ltd ,  Hamilton Medical ; travel reimbursements from  Aerogen and Fisher & Paykel Healthcare.
Using natural language processing (NLP) and machine learning to process and use clinical text for such applications has great potential  [11] [12] [13] [14]. Unfortunately, machine learning, and more specifically supervised machine learning, requires a great amount of labeled data to train a model, which is at the origin of the main bottleneck of model development  [15]. Manually labeling data sets is extremely costly and time-consuming as multiple experts need to manually review and annotate several hundreds of clinical notes  [13, 16]. Moreover, the development of such a resource presents unique challenges as the text contains personal information, and access to such data is usually restricted. Throughout the past years, shared resources such as Informatics for Integrating Biology and the Bedside (i2b2) have generated deidentified and annotated data sets for the development of NLP systems for specific tasks.
Cohort Description Our data set consisted of 20,009,822 notes from January 1, 2000, to December 31, 2016, for 134,000 patients with cardiovascular diseases from Stanford Health Care (SHC), collected retrospectively in accordance with the approved institutional review board protocol (IRB-50033) guidelines. Progress notes from outpatient office visits were selected. As the ICD-10 codes for symptoms were chosen for initial labels, encounters without R codes were discarded. Finally, short notes (ie, <350 characters) were also discarded. The final cohort contained 545,468 notes for 93,277 patients (Figure  1 ). For prototyping purposes and to evaluate the effect of the training set size on the performance, subsets of the full cohort were created, leading to the following three data set sizes: I (patients: 717/93,277, 0.
77%), II (patients: 5611/93,277, 6. 02%), and III (patients: 93,277/93,277, 100%). Patients were split into training, validation, and test sets using a 60:20:20 ratio. Table  1  provides a more detailed description of the data sets. ICD-10 codes describing symptoms and signs involving the circulatory and respiratory systems were used to label the notes for the text classification task. The symptoms considered were only coded at the highest level of the ICD-10 hierarchy. The prevalence of the R codes was low, between 2% and 10% of positive instances (see Table  S1  in Multimedia Appendix 1 for details). Our full cohort consisted of 20 million notes and 134,000 patients. We selected progress notes from outpatient visits from encounters with International Classification of Disease-10th revision (ICD-10) codes from the chapter R.
Pipeline We defined our task of extracting symptom information from clinical notes as a multi-class classification problem. Machine learning algorithms were trained to classify whether each input note contained a specific class of symptoms. The proposed pipeline used a subset of the ICD-10 chapter containing symptoms, signs, and abnormal clinical and laboratory findings. The codes in this chapter are typically used when a sign or symptom cannot be associated with a definitive diagnosis. As their occurrence in EHR is expected to be incomplete, we assumed that the presence of a code is associated with the observation of the symptom, but the absence of a code cannot be associated with the absence of the symptom in question. The full pipeline developed for this study is depicted in Figure  2. We obtained the raw clinical text and encounter data from the SHC database.
The raw text was first preprocessed for standardization purposes. Then, the text was transformed into a numerical format (ie, featurization) so that it can be used as input features for our model training. Then, ICD-10 codes were extracted from the structured encounter data to use as labels. A multi-class classification model was then trained to predict the presence of symptoms in the text. Next, we propose a weak supervision labeling pipeline as an additional method for extracting labels for the downstream prediction task. For that additional part, notes that were initially discarded because of the lack of symptom codes in the encounter data were processed using an entity recognition model with the spaCy library  [43]  and labeled using a labeling model generated using the Snorkel package  [42].
Performance Evaluation We used the following classification metrics to evaluate each model: recall, F1 score, and average precision score. We also computed the receiver operating characteristic (ROC) curves and precision-recall curves. Owing to the class imbalance, we gave more importance to the precision-recall curve. For example, in the case of hemorrhage from respiratory passages class of symptoms (R04), the positive instances represent only approximately 1% of the data points. We also considered computation time and memory requirements as important metrics to determine the best classification model. Given the size of our data set, an efficient implementation was of paramount importance for the success of our predictive model.
Enriching the Training Set With Weak Labels Enhances the Performance Further The original cohort contained many notes that do not contain ICD-10 codes from the R chapter, leading to a substantial reduction in the number of notes available to train our model. Indeed, an additional 1,290,170 notes from the patients included in our cohort did not contain any ICD-10 code for symptoms. To use these notes, they were processed using a weak supervision approach to determine the presence or absence of symptoms belonging to the R00-R09 categories. Then, the weakly labeled notes were added to data set D for training the classifier (ie, data set IV). For comparison, we also trained a model using only the weakly labeled notes (ie, data set V). Then, the 2 models were tested on test set III with ICD-10 codes for labels.
Embedding-Based Features Perform Better Than TF-IDF Features on an External Validation Set We selected a set of 56. 65% (571/1008) notes from the i2b2 2009 challenge annotated for symptom extraction  [52]  containing mentions of symptoms of cough (R05), abnormalities of breathing (R06), and pain in throat and chest (R07). The logistic regression models trained on data set III using TF-IDF and PV-DBOW features were used to predict the presence of the 3 classes of symptoms. Overall, the model trained with PV-DBOW features performed well when used to predict symptoms from the i2b2 notes. Figure  9  shows the difference in scores between the i2b2 data set and the baseline data set III trained using TF-IDF and PV-DBOW features for the set of 3 selected classes of symptoms. For R06 and R07, PV-DBOW, recall, and AUROC scores were within the range of the scores obtained when tested on the SHC notes.
This approach not only allowed us to make use of a much larger set of notes for training but also significantly improved the predictive performance, both on an SHC test set and an external validation set. Although TF-IDF features yielded the best performance overall (Figure  4 ), the size of the feature vector is the size of the corpus, leading rapidly to intractable size and computational inefficiency when the corpus size increased (Table  2 ), whereas embedding methods such as CBOW and PV-DBOW led to a fixed feature vector length, independent of the training corpus size. The main computing cost in such an approach lies in the pretraining of the embedding vectors, which must be performed only once. Training a classifier on any data set size led only to a minor increase in computational cost, making this approach more desirable.
As they did not report recall or the prevalence of each class, a direct comparison with our work is difficult. However, it is worth noting that the best-performing model on the MIMIC-III data set (using ICD-9 codes) yields a macro-F1 of 64. 85. Their best-performing model on CodiEsp (using ICD-10 codes) yields a macro-F1 of 11. 03. Our macro-F1 of 24. 66 falls in between these values, suggesting that our performance lies within the range of some of the best-performing deep learning models available. We note that although we are using a data set containing gold standard annotations, a direct comparison with previous results from Steinkamp et al  [52]  is not possible. Both experiments are fundamentally different. Our objective was to lay out strategies to generate training labels for a symptom classification task and demonstrate that if sufficient training data are provided, such strategies will yield good predictive performance.
Conclusions In this study, we introduced 2 methods to extract labels from EHR data sets for the training of a classifier for clinical notes. Multiple featurization methods were investigated, showing that PV-DBOW is clearly superior in terms of transferability and scaling. Although the use of ICD-10 codes present in the encounter data is a simple way of extracting training labels, the poor accuracy of the coding leads to less accurate models. Using a weak labeling pipeline to extract such labels yields improved performance and allows for the use of more notes as we are not relying on the presence of codes. Both approaches have been validated with an external set of notes containing gold labels, which showed the superiority of the weak labeling approach. Using ICD-10 codes for initial labels, we grouped a wide variety of signs and symptoms under the same label, learning classes of symptoms rather than specific symptoms.
Outpatient progress notes collected from January 1, 2000, to December 31, 2016, from the SHC EHR database were used to train a text classifier to extract symptoms related to JMIR Med Inform 2022 | vol. 10 | iss. 3 | e32903 | p. 5 https://medinform. jmir. org/2022/3/e32903 (page number not for citation purposes).
This experimental strategy was chosen to identify as many as possible specific and functionally relevant ITM2B partners. We performed three independent experiments with one human retina (see "Materials and methods"). Immunopurified protein complexes from each retinal sample were analyzed using LC-MS/MS in triplicates. This proteomic analysis provides the first dataset of the ITM2B interactome in the retina. The workflow of the experience is depicted in Fig. 1. Stringent filtering identified a total of 1102 proteins from immunopurified ITM2B complexes with the two anti-ITM2B antibodies. To concentrate on specific interactors, only ITM2B protein partners with a fold change > 2 (FC > 2) compared to the negative control and a p-value < 0. 01 were retained. Considering only specific interactors, (FC > 2 and p-values < 0.
01) a total of 457 putative ITM2B interactors were identified in the human retina (Supplementary Table  S1 ). The vast majority of the identified proteins are not reported as specifically expressed in the retina. Indeed, we compared our dataset with a Human Protein Atlas database list of 310 proteins highly expressed in the retina compared to other tissues. Among the 457 putative ITM2B interactors, only 9 proteins are highly expressed in the retina (Supplementary Table  S2 ). These proteins are almost all exclusively expressed in photoreceptor cells and their associated biological processes are visual perception and photoreceptor development (Supplementary Table  S3 ). However, we also identified interactors localized in all retinal cell types. Indeed, among 457 ITM2B potential interactors, we found tubulin beta-3 class III (TUBB3) and the neurofilament light polypeptide (NEFL) both highly expressed in retinal ganglion cells  20, 21.
We thus concluded that proteins with GO-BP or CC terms associated with the mitochondria were purified with only one of the two antibodies. To describe more precisely the protein complexes immunopurified by each antibody we analyzed ITM2B interactors with unsupervised hierarchical clustering. We then established a heatmap of identified proteins according to the antibodies used for protein complex purification (Fig. 2b ). Beside the 140 common proteins, we also found two distinct clusters of 114 and 220 proteins specifically purified with the mouse and rabbit anti-ITM2B antibody respectively (Fig. 2b , Supplementary Fig. S4  and Fig. S5 ). To better characterize these two specific clusters, each dataset was studied separately. ITM2B interactome purified specifically with the mouse anti-ITM2B antibody.