dataesr/scientific-paragraphs-categorization huggingface dataset:
This option revealed several interesting observations when OPTIMIR was applied to MARTHA plasma samples. First, it allowed the identification of improperly imputed genotypes despite overall good imputation quality. Second, it suggested the existence of a new miRNA not indexed in the miRBase v21 database that would be located on the opposite strand of the hsa-miR-1255b-5p. Thirdly, it suggested that reads aligned to hsa-miR-7107-3p are likely false alignments and would more likely come from other noncoding fragments that share sequence similarity with hsa-miR-7107-3p. These last two hypotheses would need to be further validated but this is out of the scope of the bioinformatics workflow described in the current work. Even more interesting was the study of polymiRs' expressions in heterozygous individuals for SNPs on these polymiRs.
The former tends to have little impact on the general findings (see Supplemental Table  S2 ), while the second may be study-specific and may depend on the study sample-size and the kind of tissue analyzed. These parameters can be easily modified by the user. There is so far no gold standard program for miRNA alignment analysis but our preliminary study suggests that OPTIMIR aligns slightly less reads than two other recently proposed software, sRNAnalyzer  (Wu et al. 2017)  and miRge  (Baras et al. 2015). This is likely due to the higher number of mismatches allowed by the latter two for aligning reads while OPTIMIR tends to be more stringent. Without extensive investigations including experimental validations, it is not possible to really appreciate which alignments are the correct ones. Finally, several improvements could be considered such as (i) the integration of A-to-I editing events in the definition of our reference library and of our scoring strategy, even if we anticipate that it might be difficult to distinguish these rare events from sequencing errors and (ii) the extension of the OPTIMIR workflow to analyze other small coding RNAs (e.
The MARTHA data set The MARseille Thrombosis Assocation study is a collection of patients with venous thrombosis (VT) recruited at the La Timone Hospital (Marseille, France) between 1994 and 2005 and aimed at identifying novel molecular determinants for VT and its associated endophenotypes  (Oudot-Mellakh et al. 2012; Dick et al. 2014). For the present study, 391 MARTHA participants with available plasma samples were processed for plasma miRNA profiling through miRSeq. These individuals had been previously typed for genome-wide genotyping arrays and imputed for single nucleotide polymorphisms (SNPs) available in the 1000G reference database  (Germain et al. 2015). MiRNA extraction and preparation followed the same protocol as the one previously described in  Roux et al. (2018). Briefly, from 400 µL of plasma, total RNA was first extracted using the miRNeasy serum/plasma kit for Qiagen.
The OPTIMIR workflow Alignment Prealignment data processing. 3 ′ adapters were removed using cutadapt  (Martin 2011)  with a base quality filter set to 28. Remaining reads with sequence length between 15 and 27, which generally correspond to miRNA sequences, were then kept for alignment. Note that identical reads were collapsed together to decrease the computational burden associated with processing n times n identical reads. Definition of an alignment reference library. Read alignment generally starts by the selection of a reference library to which reads shall be aligned. The miRBase 21 database  (Kozomara and Griffiths-Jones 2014)  containing known human mature miRNAs is usually adopted for miRSeq data. We first upgrade this reference library by adding new sequences corresponding to the alternate forms of polymiRs showing genetic polymorphisms in their mature sequence as previously proposed  (Baras et al.
2015; Russell et al. 2018). In case a polymiR contains more than one polymorphism, new sequences corresponding to all possible haplotypes are generated. These variants, that are provided by the OPTIMIR's user in a vcf format file  (Danecek et al. 2011) , are mapped to miRNAs using miRBase miRNA coordinates file (i. e. , positions of miRNAs on the human reference genome). The generation of new sequences is automated via a standalone python script provided with the OPTIMIR pipeline. For the current application to MARTHA GWAS data, we identified 88 single nucleotide polymorphisms (SNPs) for which we have a reliable genotype data, defined as SNPs with imputation r 2 > 0. 8. Note that some SNPs may map to two distinct miRNAs if the latter are transcribed from opposite strands. Some miRNAs may also have more than one SNP in their sequence.
In our application, five SNPs mapped to two distinct miRNAs and three miRNAs contained more than one SNP (see Supplemental Figure  S2  for examples). As a result, the reference library was upgraded with 96 new alternative sequences corresponding to all possible haplotypes derived from the 90 identified polymiRs. Read alignment process. For read alignment, we opted for the bowtie2 software  (Langmead and Salzberg 2012)  that can handle trimming and tailing events at the reads' extremities via its local alignment mode which has been shown to be efficient for miRSeq data alignment  (Ziemann et al. 2016). Only reads with a sequence of at least 17 consecutive nucleotides (defined as the alignment seed) that perfectly match with the reference library are kept in the analysis (see Supplemental Table  S1  for details concerning the choice of the seed value and its consequences on isomiR detection).
Genotype consistency analysis In case users provide genetic information for individuals that have been miRSeq profiled, the last step of the OPTIMIR workflow is to provide a comparison analysis of the genotype data provided by the user (in a standard vcf format) and the genotype data that could be inferred from the sequenced reads aligned to polymiRs. For an individual whose reads aligned onto a polymiR sequence that harbors the alternate allele of a SNP, consistency will be called if this individual is either heterozygous or homozygous for this allele in the provided vcf genotype file. Inconsistent alignments are discarded but saved in an output file (named inconsistents. sam). Indeed, it may occur that some reads align to a polymiR sequence that harbors a given allele in an individual that is not expected to carry it.
ACKNOWLEDGMENTS F. T. and M. R. were financially supported by the  GENMED Laboratory of Excellence on Medical Genomics ,  Agence Nationale de la Recherche  ( ANR-10-LABX-0013 ). D-A. T. was financially supported by the  "EPIDEMIOM-VTE" Senior Chair  from the  Initiative of Excellence of the University of Bordeaux. MiRNA sequencing in the MARTHA study was performed on the iGenSeq platform ( ICM Institute, Paris ) and supported by a grant from the  European Society of Cardiology for Medical Research Innovation. Received November 26, 2018; accepted February 21, 2019.
Readmission in the intensive care unit (ICU) is associated with poor clinical outcomes and high costs. Traditional scoring methods to help clinicians deciding whether a patient is ready for discharge have failed to meet expectations, paving the way for machine learning based approaches. Freely available datasets such as MIMIC-III have served as benchmarking media to compare such tools. We used the OMOP-CDM version of MIMIC-III (MIMIC-OMOP) to train and evaluate a lightweight tree boosting method to predict readmission in ICU at different time points after discharge (3, 7, 15 and 30 days), outperforming existing solutions with an AUROC of 0. 802 (SD=0. 011) and a recall of 0. 837 (SD=0. 016) for 3-days readmission.
Introduction Recent studies have shown that readmission in the intensive care unit (ICU) is associated with poor clinical outcomes, increased length of ICU and hospital stay, and high costs  (1, 2). One of the main reasons for ICU readmission that has been identified is premature discharge  (3) ; in fact the transfer of patients from an ICU to a general hospital ward represents a high-risk event, and thus the decisions about which patients are ready to be discharged are daily struggles for ICU clinicians  (4). Other studies have shown that determining the best timing for ICU discharge is usually based on subjective intuitions and that readmission prediction tools can help physicians in this endeavor, provided their performance and ease of adoption  (5, 6). As traditional scores based on logistic regression or Cox proportional hazards models such as the Stability and Workload Index for Transfer score (SWIFT) or the LACE index have failed to meet expectations  (6) (7) (8) (9) (10) , numerous prediction models using machine learning have been proposed in the recent past, several of which trained and evaluated on the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-II or MIMIC-III) open database  (11) (12) (13) (14) (15).
MIMIC-III is a large ICU EHR database widely accessible to researchers internationally under a data use agreement, allowing clinical studies to be reproduced and benchmarked  (16, 17). In order to make multicenter ressearch possible, a valuable effort has been made to convert MIMIC-III to the Observational Medical Outcomes Partnership common data model (OMOP-CDM), which provides structural and conceptual models relying on international reference terminologies  (18, 19). For the sake of reproducibility and ease of subsequent implementation in other centers using the same data model, we chose to use the OMOP-CDM version of MIMIC-III (MIMIC-OMOP), for which documentation and a mapping Extract-Transform-Load (ETL) process are freely available.
Methods Data and patients. MIMIC-III integrates deidentified, comprehensive clinical data of patients admitted from 2001 to 2012 at the Beth Israel Deaconess Medical Center in Boston, Massachusetts. We restricted our analyses to ICU stays of patients over 18 years old, ending up with a dataset of 55136 stays. Variables used in the model were provenance from a surgery ward, age, gender, length of stay, current count of ICU visits for the same patient, and three values for a number of measures and blood tests: the first entry for a given stay, the last one (which could be the same as the first) and the absolute difference between the two. Those measures were total glasgow coma scale (GCS), motor GCS, verbal GCS, eye movement GCS, systolic blood pressure, heart rate, respiratory rate, body temperature, oxygen saturation, oxygen inspired fraction, body weight, urine output, serum bicarbonate, serum urea, total bilirubin, serum sodium, serum potassium, serum creatinine, blood platelets, hemoglobinemia, blood hematocrit, blood leukocytes, serum lactates, blood PH, blood glucose and the International Normalized Ratio (INR).
Three variables were extracted from medical and nurses text notes: history of AIDS, metastatic cancer and/or of advanced hematologic condition (myeloma, lymphoma or leukemia). Finally, one feature counting the number of available values for all the previous variables was added. Missing data were then imputed via multivariate imputation  (20) , with a Bayesian ridge regression as the estimator  (21). We used a similar outcome definition to by Lin et al. (12) , where positive cases were regarded as the ones where the patients could benefit from a prediction of readmission before being transferred or discharged: patients who were transferred or discharged but returned to ICU or died before a defined time limit (3, 7, 15 or 30 days). Models. Several models were tested in a screening phase. Among those, XGBoost, a gradient tree boosting method that is widely used by data scientists to achieve state-of-the-art results on many machine learning challenges, was consistently more performant than the others and was thus selected as the prediction model for this study  (22).
Stratified cross-validation was used in two ways; 5-fold for hyperparameter selection in a grid-search setting, and 10-fold for model evaluation. Calibration of the models were assessed by separating the predicted probabilities over the whole dataset into deciles, and assessing the proportion of real outcomes in each bin. A model is said to be well calibrated when each bin's true outcomes proportion is close to the bin's predicted probabilities, resulting in a calibration curve close to the diagonal line when plotted. Contributions of each feature to the final model were reported via feature importance; the more a feature is used to separate groups within the model's trees, the higher its relative importance. It is important to note that this might not necessarily mean that features with a higher importance are significantly associated with the outcome in real life, but it is nonetheless a good way to assess what the model has learned.
Discussion While this work improves on existing solutions for ICU readmission prediction as measured by commonly used metrics, several points still need to be addressed. The prediction model was trained and evaluated on MIMIC-OMOP, and as of this time, no external validation has been conducted yet. To facilitate this process, all code needed to reproduce the results has been made freely available, and efforts have been put into its ease-of-transfer on any other electronic health records (EHR) database using the OMOP-CDM standard. External validation is planned to be conducted on several French EHR databases. Further work needs to be done to integrate and evaluate such prediction models in real life settings. The choice of developing on common health data standards such as OMOP-CDM and/or HL7's Fast Health Interoperability Resources (FHIR) (  23 ) is a step forward in this direction.
Conclusion In this study, we proposed a model based on a tree boosting method to predict ICU readmission at 3, 7, 15 and 30 days using data of patients at discharge on the freely available MIMIC-III database. With a 3-days readmission AU-ROC of 0. 802 (SD=0. 011) and recall of 0. 837 (SD=0. 016), or solution outperforms existing ones and has the advantage of having been conceived with the OMOP-CDM standard, allowing for easier external validation and implementation. Each feature prefixed by "first_", "last_" and "delta_" represent the first measure in the current stay, the last one and the difference between the two respectively. "full_count" represents the amount of non-missing features of an individual for a given stay.
Analysis of blood samples Fifteen millilitres of peripheral venous blood was collected in K2 EDTA tubes before initiation of Bv and then before the third, fifth and seventh cycles of treatment. The flowchart of the study design is illustrated in Sup. Figure  1. For each point, a complete blood cell count (CBC) was performed on a Beckman Coulter haematology analyser. A six-colour flow cytometric analysis on fresh blood was also performed using a BD FACSCanto II cytometer (Rennes) or a Beckman cytometer (Paris). Flow cytometry analyses were performed using FACSDiva (BD Biosciences) and Kaluza (Beckman Coulter) software. The gating strategies are described in detail in Figure  S2. Negative controls were included for defining accurate gating. Preliminary tests made it possible to ensure that identical results were obtained at the two sites carrying out the analysis.
Statistical analysis Data were described using median [minimum-maximum] values concerning quantitative variables and frequencies n (%) for categorical variables. Biological counts were reported in the form of cell counts and percent out of cell populations of interest (e. g. % of Treg among CD3 + T cells). Box plots were presented to draw evolution of biological counts at each sample time: before C1 (the first cycle noted C1), C3, C5 and C7. Evolution over time of the cell populations was tested through one-way ANOVA with repeated measures. Further pairwise comparisons from the baseline value (C1) were conducted with Wilcoxon matched pairs signed-ranks tests. No adjustment for alpha risk inflation was performed, but figure presentation allows reproducing such kind of reasoning by distinguishing p < 0.
Studied populations Twenty-nine patients (21 in Rennes and 8 in Paris) were enrolled in the prospective study from September 2012 through December 2014. The median overall survival was 8. 5 months  [6. 8-18. 4 ]. To constitute the validation cohort 1, all the GBM patients having received Bv at recurrence, not having participated in the prospective protocol and for whom a total blood count was available within 10 days before the start of treatment were extracted from the local database. Sixty-one patients treated between December 2008 and May 2017 were selected. Among this cohort, 60% of patients received Bv alone as a second-line treatment and 56% received a combined treatment (Bv and chemotherapy) as a second or third line. In the prospective cohort, half of the patients received a combined treatment (Bv and chemotherapy) after failure of Bv as monotherapy, which makes these two cohorts relatively similar.
This ratio is regarded as a marker of systemic cancer-associated inflammation. In our prospective cohort, we observed that a median basal NLR of 5. 2 and patients with a value above the median had no significant shorter median overall survival (7. 2 months versus 17. 5 months; p = 0. 083, data not shown). However, a prognostic value was obtained when considering the neutrophil count alone. Such an association between blood baseline neutrophil count and Bv efficacy in GBM has previously been reported, but with contradictory results as compared with ours. In the retrospective cohort reported in the study of Bertaut et al. [27] , GBM patients Fig. 1  Immune cells with significant variations during bevacizumab treatment. A complete blood cell count (CBC) and a cytometric analysis were assessed at baseline (C1) and before the third (C3), the fifth (C5) and the seventh (C7) cycles of treatment.
The average cost is ≈ 500$. Mechanical parts and rotors are not included and the additional cost to build a DIY 8-rotors UAV ranges between ≈ USD $400 and USD $750. It is important to mention that the essential component of a UAV-based system for agricultural applications is the sensor or multispectral camera. Most common commercial options include RedEdgeMX from Micasense (http://www. micasense. com), ADC-micro from Tetracam Inc. (http:// www. tetracam. com), and Sequoia+ from Parrot (http:// www. parrot. com), with prices ranging from ≈USD $3, 000 to USD $5, 500. The application cited in this manuscript was conducted with the ADC-micro model, a 3-band Agricultural Digital Camera (ADC) manufactured by Tetracam (the term TTC is used to refer to this device). It is a 90-g RG-NIR (red, green, and near-infrared) camera, specifically designed for operation aboard UAVs due to its small dimensions (75 mm × 59 mm × 33 m).
In vegetation studies, the spectral region with central wavelengths green (∼560 nm), red (∼665 nm) and near-infrared (∼842 nm) are the most informative bands used to calculate vegetation indexes such as NDVI  (Rouse et al. 1973). Widely used commercial spectrometers are USB2000+ from Ocean Optics (http:// www. oceanoptics. com), SensLine from Avantes (http:// www. avantes. com), and Glacier spectrometer from Edmund Optics (http://www. edmundoptics. com) with prices that range from ≈ USD $2,000 to USD $4,000. For IMAGRI-CIP implementation, we selected interference filter wavelengths based on spectral studies carried out with the USB2000+ Ocean Optics Spectrometer. For this, we developed an additional tool called SpectraCIP (see Fig. 2 ), which aims to facilitate the spectra acquisition through a user-friendly software under open-access policy.
Spectra-CIP is a C-language-based graphical user interface (GUI) software. Hence, the user is able to control several acquisition parameters such as exposure time, the number of samples to average, binning, among others. Also, it can acquire reference measurements (using a Lambertian surface) to ultimately calculate the vegetation reflectance. The software has been tested with the USB 2000+ model for visible and NIR input light. This software is available to be used by the agricultural remote sensing community and can be downloaded from  Loayza et al. (2017).
5 , and an example of a stitched image is shown in Fig. 6. This mosaic was generated using 16 multispectral images acquired with the commercial agricultural ADC-micro Tetracam (TTC) camera over avocados at the National Institute for Agricultural Innovation (INIA) -Huaral, Peru facilities in April 2016. The resulting image has a total area of 375 m × 180 m acquired with six ≈8 min UAV flights at an altitude ≈120 m. ISAM-CIP can be downloaded from  Loayza et al. (2017a). These pre-processing steps can also be performed by commercial solutions. For example, PIX4Dmapper from PIX4D (http://www. pix4d. com) and Agisoft Metashape from Agisoft (http://www. agisoft. ca) provide the needed tools to align and stitch UAV-based images. In most cases, the data processing is carried out in a "cloud," i. e.
opencv. org for further details). During the period August-September 2015, image acquisition was performed five times, and two sets of flight paths were designed with the microkopter UAV for each camera. First, we used the TTC camera, and a ground measurement of a reference (Teflon calibration plate) was acquired to get the NDVI values through the Tetracam software PixelWrench2. After that, we assembled the TTC camera to the UAV, and a single image was acquired at    selected (shown in colors) to conduct the comparative assessment between the two camera systems. For each grid, four sub-grid columns containing NDVI values were analyzed. Their mean and standard deviations were used to compare the multispectral camera performance. As an example, results from the statistical analysis for the red grid for August 17, 2015, are shown in Table  1.
Then, the images were geo-referenced with QGIS software and stitched with the ISAM-CIP software. The resulting RGB mosaic is shown as Fig. 10 , whereas the TTC false-color mosaic, composed of the NIR, red and green bands, is shown in Fig. 11. Supervised classification for both RGB and TTC mosaics was performed using the Maximum Likelihood (ML) method, including statistical textures as a complement to original bands to create more robust feature vectors. Thus, mean and standard deviation were calculated for the red and NIR bands from the RGB and TTC mosaics, respectively, using Kernel sizes of 3 by 3 pixels. The following classes were discriminated: mango, banana, grassland, cowpeas, sunflower, sweet potato, maize 1 (around 2 weeks after planting), maize 2 (about 1 month after planting), paddy rice, rice 1 (around 2 weeks after planting), rice 2 (approximately 1 month after planting), bare soil and road.
On the other hand, the TTC camera has only one optical sensor that estimates the integration time of two images, red and green, since it only computes with a high dynamic range the NIR image. For this, the TTC camera set its optimal exposure time based on the NIR input, which might saturate the sensor faster. It is therefore reasonable that NDVI estimations in both multispectral systems may not be necessarily the same. Conceptually, IMAGRI-CIP provides a higher signal-to-noise ratio data and might improve the measurements of NDVI values. Moreover, the advantage of the IMAGRI system is its capacity to adapt to and measure different regions of the light spectrum. In this context, the innovative SpectraCIP software helps to analyze spectral signatures of vegetation canopy at ground level. With this information, vegetation studies can be assessed under different types of abiotic and biotic stress conditions making it possible to identify specific spectral regions responding to particular problems affecting the plants.