dataesr/scientific-paragraphs-categorization huggingface dataset:
FUNDING The SafeSpace project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 870437. The project Plasmasphere ionosphere Thermosphere Integrated Research Environment and Access services: a Network of Research Facilities PITHIA-NRF (https://www. pithia-nrf. eu/) has received funding from INFRAIA-02-2020, ID 101007599. Processing and analysis of the (HOPE, MagEIS, REPT, or ECT) data was supported by Energetic Particle, Composition, and Thermal Plasma (RBSP-ECT) investigation funded under NASA's Prime contract no. NAS5-01072.
Clinical Outcome Out of the 31 patients, clinical benefit could be evaluated in only 24 patients. Indeed, seven patients were lost to follow-up before response evaluation and had to be removed from the final evaluation for clinical response. Clinical benefit was observed in 13 patients (54. 1%). Two patients (i. e. , 8. 3%) achieved complete response, 11 (45. 8%) had stable disease, 11 (45. 8%) had progressive disease. Four of the seven patients lost to follow-up also had a lack of clear information concerning safety data (i. e. , no precise grading for the non-hematological toxicities in hospital records) and thus could not be properly evaluated for sunitinib-related toxicity. When considering early-onset toxicities (i. e. , side effects showing from the first month after treatment initiation), 17 out of the 27 evaluable patients (i.
Of note, the fact that in this study, globally, no relationship between baseline exposure (i. e. , Cmin or AUC) and clinical efficacy was found can be partly explained by the fact that, here, drug exposure was solely measured at baseline, and not after subsequent empirical changes in dosing. As efficacy was evaluated 3 months after that treatment had started, the subsequent changes in dosing in 53. 8% of patients were thus a major confounding factor when trying to find a relationship between initial drug exposure and clinical outcome 3 months later. In this respect, here, the collected data on efficacy and the possible usefulness of TDM plus adaptive dosing to improve efficacy cannot be conclusive, an observation already reported by others for mRCC patients  [14]. This calls for implementing longitudinal monitoring of sunitinib concentrations, especially when patients are likely to have their dosing changed over time.
Patients All routine patients with a diagnosis of metastatic RCC of any histology and scheduled for sunitinib treatment from 2017 to 2020 in our institute were considered for monitoring. All patients were treated with standard sunitinib in the Medical Oncology Unit at La Timone University Hospital of Marseille. Patients were identified from the hospitals' administrative database, and relevant information regarding empirical changes in dosing, efficacy and toxicity endpoints were retrieved from electronic medical records. All patients underwent therapeutic drug monitoring (TDM) as part of routine care in our institute with oral targeted therapies. Data exploitation after anonymization was granted upon the non-opposition principle at the Assistance Publique Hôpitaux de Marseille institute (i.
efficacy and toxicity endpoints were retrieved from electronic medical records. All patients underwent therapeutic drug monitoring (TDM) as part of routine care in our institute with oral targeted therapies. Data exploitation after anonymization was granted upon the non-opposition principle at the Assistance Publique Hôpitaux de Marseille institute (i. e. , unless the contrary is stated, all data collected during routine care can be used for biomedical research). A time line of the study is provided in Figure  7.
Statistical Analysis All statistical analyses were performed using MedCalc software 4. 1. (Belgium). Depending on data distribution (i. e. , normal distribution, Kolmogorov-Smirnov testing) and equal variance testing, a t-test or non-parametric Pearson test were considered for statistical analysis. Typically, Pearson testing was performed when normality or equal variance testing failed, or when statistical power below 0. 8 prevented a t-test from being run. Pre-checking for normality, equal variance or statistical power was performed with Sigma Stat 2. 1. (SPPS, Germany). A p value of 0. 05 was regarded as statistically significant.
Reproducibility is a cornerstone of scientific communication without which one cannot build upon each other's work. Because modern human brain imaging relies on many integrated steps with a variety of possible algorithms, it has, however, become impossible to report every detail of a data processing workflow. In response to this analytical complexity, community recommendations are to share data analysis pipelines (scripts that implement workflows). Here we show that this can easily be done using EEGLAB and tools built around it. BIDS tools allow importing all the necessary information and create a study from electroencephalography (EEG)-Brain Imaging Data Structure compliant data. From there preprocessing can be carried out in only a few steps using EEGLAB and statistical analyses performed using the LIMO EEG plug-in.
, 2020)  and all steps proposed can also be reproduced from the user interface. We refer to the extensive EEGLAB online user manual for GUI operations, and simply point that functions called by interface operations in EEGLAB are saved into the EEG. history field, thus allowing to copy/paste the underlying code to build a different pipeline than the one proposed here. EEGLAB is used here in conjunction with newly developed EEG-BIDS 1  tools which allow automatically importing and create a STUDY (see Figure  1 ) for data that follow the EEG-Brain Imaging Data Structure and with the LIMO-EEG toolbox which allows statistical analyses using robust hierarchical linear models  (Pernet et al. , 2011). The overarching goal of this analysis is to show that it is possible to use a simple pipeline to process "raw" data and perform complex statistical analyses using EEGLAB and LIMO.
Open Data The pipeline was executed using the EEG data from the multimodal face dataset  (Wakeman and Henson, 2015). EEG (70 channel Easycap 2  ) and ECG data were extracted from the binary MEG. fif files that combined MEG, EEG, ECG channels, event markers were time corrected (-34 ms) and electrode positions re-oriented to fit the head coordinate system. Out of the 19 participants, participant 1 was removed because of channels digitization errors leading to 18 participants. Data were then organized using EEG-BIDS and archived for download at https://openneuro. org/datasets/ds002718. This dataset is thus a modified and simplified version of the original dataset, with a subset of this original dataset itself made available by authors at https://openneuro. org/datasets/ds000117. In the original data, there were multiple runs for each subject, so we have merged them to make it simpler for users.
We have also formatted scanned electrode positions so they are available within the BIDS dataset. We have corrected event latencies, renamed some events, and added information on events and stimulus repetition. We also resampled the data to 250 Hz so it is not as large and can be used for tutorials. All modifications to the original data documented in the readme file of BIDS ds002718 and conversion script are also made available. The experiment consisted of the presentation of 300 grayscale photographs of familiar and unfamiliar faces, along with their scrambled versions, all repeated twice. Trials started by a fixation cross lasting 400-600 ms, followed by stimuli lasting from 800 to 1000 ms and the repetition of the images occurred either immediately or after 5 or 15 stimuli, leading to a range of time intervals between repeats.
Software Analyses are performed using Matlab 2020a (The Mathworks, Inc. 3  ) on Windows or Mac OSx with the Statistical and Machine Learning Toolbox installed, along with EEGLAB 4  (v2020. 0) and its BIDS tool 5  (v3. 5) and LIMO EEG 6  (v3) plugins-both of them available through the EEGLAB plugin manager.
BIDS-Import The EEGLAB BIDS plugin allows importing BIDS datasets as EEGLAB studies. The plugin allows overwriting events and channel information contained in the raw EEG data-we are using both of these options here. This allows, for example, to define more precise events and channel information-such as channel locations derived from scanned Polhemus positions. The plugin also allows selecting specific fields in the BIDS event file (in this case we used "trial_type" to be mapped to the EEGLAB "type" field). This is reflected in the call to pop_importbids. m function in Figure  2.
Fully Automated Preprocessing We present here a workflow with minimal preprocessing (Figure  2 ), which is a set of steps that removes common artifacts without trying to optimize any particular data features, using fully automated methods. Note that, as mentioned earlier, the goal is to present a fully reproducible workflow of ERP analyses with EEGLAB, and not to reproduce or replicate  Wakeman and Henson (2015). As such, our preprocessing differs from Wakeman and Hensons' workflow which, in any case, was primarily designed for the associated MEG data, starting with Signal Space Separation for noise removal  (Taulu and Simola, 2006) , then bad channel removal, and notch filtering. As explained above, the code underlying the different steps can be obtained from EEG. history when using the GUI to process subjects.
First, bad channels are removed and data filtered at 0. 5 Hz using clean_rawdata plugin of EEGLAB (v2. 2) and the pop_clean_rawdata. m function (transition band [0. 25 0. 75], bad channels defined as a channel exhibiting a flat line of at least 5 s and/or correlation to their robust estimate based on other channels below 0. 8). Second, data are re-referenced to the average (pop_reref. m) and submitted to an independent component analysis (pop_runica. m using the runica algorithm and a reduction in rank to the number of channels -1 to account FIGURE 2 | Code used for importing and preprocessing. The region highlighted in red must be changed by the user to point to the location of the data on his/her hard drive. A version of this code that can be copied and pasted is available in the LIMO MEEG repository: https://github.
com/LIMO-EEG-Toolbox/limo_ meeg/tree/master/resources/from_bids2stats. m. Note that this part of code was automatically generated from EEGLAB and EEGLAB plugins menus by using the command history function eegh-and then edited for clarity. for average reference). Third, each component is automatically labeled using ICLabel  (Pion-Tonachini et al. , 2019) , rejecting components labeled as eye movements and muscle activity above 80% probability. Finally, continuous data are further cleaned if their power deviated too much from the rest of the data using Artifact Subspace Reconstruction (ASR) algorithm and a 20 burst detection criteria threshold  (Kothe and Jung, 2016; Chang et al. , 2018)  thus taking care of remaining (e. g. , line noise, trends) artifacts (pop_clean_rawdata. m, burst criterion 20).
Statistical Modeling From the clean continuous data, epochs are created by extracting data snippets time-locked (from -500 ms to 1 s) to the face presentation events (pop_epoch. m) and designs created within the EEGLAB STUDY. A STUDY in EEGLAB is a structure that contains all the information about the data and metadata allowing to create any experimental designs. Here, we made two designs (std_makedesigns. m): one that recreates the faces vs. scrambled effect described by  Wakeman and Henson (2015)  and one that tests if the time between repetitions of the same stimuli influences the event-related potentials (ERPs-see section "Results" for more details on the statistical models). From first level estimates, repeated measures ANOVA were computed at the group level (limo_random_select. m). The goal is to illustrate the flexibility of linear models as implemented in LIMO EEG and ease to create such models using EEGLAB STUDY (Figure  3 ).
Model 2: Regressing the Time Between Stimulus Presentation From Trial to Trial At the first level, data from each participant were modeled with three experimental conditions (famous faces, scrambled faces, and unfamiliar faces) along with the time between the repetition of each stimulus (Figure  5 ) and a weighted least squares solution used to obtain parameter estimates of each condition. At the second level, a repeated measure ANOVA (generalized Hotelling T 2 ) was conducted on beta estimates of the time regressors with "faces" as factors, and a post hoc contrast computed, testing if the effect of time for famous faces differed from other stimuli. Statistical significance was again assessed using spatial-temporal clustering. The post hoc contrast revealed a significantly stronger modulation of ERP as time passed between famous faces than for other stimuli (Figure  5 -cluster 1 starts at 536 ms and ends at 576 ms, maximum 15.
DISCUSSION Following community guidelines  (Pernet et al. , 2020) , we have implemented necessary changes and new tools allowing to create fully reproducible workflows with EEGLAB. The pipeline for the presented analysis is available at https://github. com/LIMO-EEG-Toolbox/limo_meeg/tree/master/resources/from_bids2stats. m and further designs presented on the LIMO MEEG GitHub website (via the user interface and command-line alike https://github. com/LIMO-EEG-Toolbox/limo_tools/wiki). A key development for EEG reproducibility is the recent development of EEG-BIDS  (Pernet et al. , 2019)  which not only structures how data are organized and shared but also populates many of the metadata necessary for data analysis. The newly developed BIDS tools 7  used here allow to import such data and create automatically an EEGLAB STUDY with the different experimental conditions.
Note that from raw data imported into EEGLAB, those tools allow just as easy to export in the BIDS compliant format. While raw EEG data files often define channel labels, EEG-BIDS defines channel properties and associated labels (channels. tsv) corresponding to the electrodes for which locations are defined (electrodes. tsv) given a reference coordinate system (coordsystem. json). BIDS-Matlab-tools will always check for consistency between the data and BIDS meta-data and users have the choice on which information to use (in Figure  2 , pop_import_bids parameter "bidschanlocs"). Similarly, raw EEG data files typically contain behavioral and experimental events; these are also defined in BIDS with separate text files (events. tsv). Sometimes the BIDS event files contain different information than the raw EEG data file and users have options to choose which one of the two types of event information to import (in Figure  2 , pop_import_bids parameter "bidsevent").
Here, when preparing the BIDS dataset with EEG data only, events. tsv file was prepared as to include the nine experimental conditions but also the repetition order, distance, and time between repetitions which allowed to create the proposed design automatically. Data preprocessing is performed here by first cleaning the raw data using the clean_rawdata EEGLAB plugin (v2. 2), then by running the Infomax Independent Component Analysis algorithm (runica function of EEGLAB 2020. 0) and performing automated ICA component labeling (ICLabel v1. 2. 6). EEGLAB includes a variety of algorithms and other approaches can be implemented to preprocess data automatically. One key development for reproducible EEG artifact reduction is the use of the ICLabel EEGLAB plugin which labels independent brain and non-brain components automatically thus allowing to remove artifactual components in a consistent manner  (Pion-Tonachini et al.
Early pulmonary nodule detection is very important in lung cancer diagnosis and screening. Most stateof-the-art lung nodule detection models are based on Faster Region-based Convolutional Neural Network (Faster R-CNN) due to its superior performance. However, this object detection approach faces difficulties with the variety of nodule sizes in training datasets. In this paper, we propose a novel Computer-Aided Detection (CAD) system based on Faster R-CNN model with adaptive anchor box for lung nodule detection. Our method employs ground-truth nodule sizes in the training dataset to generate adaptive anchor box sizes of Faster R-CNN. Learned anchors are used as hyper-parameter to boost Faster R-CNN's detection performance. A residual convolutional neural network is proposed to reduce false positives from Faster R-CNN's output.
Our method is trained and tested on the largest publicly available LUNA16 dataset. Experiments show that our proposed system achieves a high sensitivity of 95. 64% at 1. 72 false positives per scan, and a Competition Performance Metric (CPM) score of 88. 2%, which outperforms other recent state-of-the-art detection methods. The false positive reduction network achieves a sensitivity of 93. 8%, specificity of 97. 6% and accuracy of 95. 7%. An additional evaluation on a completely independent SPIE-AAPM dataset demonstrates the generalization of our proposed model with 89. 3% sensitivity.
Most current methods employ the default configuration or several manually designed configurations for anchor box sizes. These approaches, however, are uncertain to fit with the real lung nodule sizes and shapes. In a direction of improving Faster R-CNN performance in pulmonary nodule detection task with various dimensions, we propose a novel CAD system to detect lung nodules in CT images using adaptive anchor sizes for Faster R-CNN. Our method is based on Faster R-CNN due to its superior performance when compared with other object detection methods. The main contributions of our work are summarized as follows: 1) In the nodule candidate detection stage, we enhance sensitivity detection performance of Faster R-CNN model by employing Mean-shift clustering technique  [12]  to automatically learn anchor box configurations from ground-truth nodule sizes in the training dataset; 2) In the false positive reduction stage, we propose a residual convolutional neural network based on ResNet architecture  [13]  to improve prediction quality by reducing false positive rate with the trade-off of decreasing some sensitivity; 3) Comprehensive experiments conducted on the publicly available LUNA16 dataset  [14]  show the effectiveness of our proposed method, which are better than other recent state-of-the-art pulmonary nodule detectors.
II. RELATED WORKS Most existing lung nodule candidate detection methods in the first phase are based on deep learning object detection networks such as You Only Look Once (YOLO) and its variants  [16] , RetinaNet  [17] , or Faster R-CNN  [9]. Among these methods, Faster R-CNN usually yields leading detection performance  [18]  and hence gains more research attention  [10]. For example,  Ding et al. , 2017 [19]  introduced a deconvolutional structure into Faster R-CNN for nodule candidate detection from axial slices, and a 3D Deep Convolutional Neural Network (DCNN) is then exploited to reduce the number of false positive samples. This method achieved 94. 6% sensitivity at 15 false positives per scan for candidate detection on LUNA16 dataset. Zhu et al. , 2018 [20]  proposed a 3D Faster R-CNN for lung nodule detection and deep 3D dual path for nodule classification.
The authors obtained a CPM score of 84. 2% for nodule detection on the same dataset. Su et al. , 2020 [3] , through experiments, performed hyper-parameter fine-tuning and optimization on Faster R-CNN detection network and achieved 91. 2% average precision on LIDC-IDRI dataset  [21]. Li et al. , 2019 [22] , evaluated various variants of Faster R-CNN on a 3D thoracic magnetic resonance image private dataset. The fine-tuned model achieved 85. 2% sensitivity at 3. 47 false positives per scan. Several works followed the same direction of fixing anchor box sizes for Faster R-CNN in order to achieve better performance of lung nodule candidate detection. Xie et al. , 2019 [23]  proposed to use fixed-size anchors (12 × 12, 18 × 18, 27 × 27, 36 × 36, 51 × 51, 75 × 75 and 120 × 120) in junction with two regional proposal networks.
This approach achieves 86. 42% sensitivity for candidate detection on LUNA16 dataset. Wang et al. , 2019 [24]  proposed a nodule-size-adaptive method based on Faster-RCNN for candidate detection from 3D CT images. Their idea is to use an estimation of nodule aspect ratios as 0. 6, 1. 0 and 1. 65 to detect nodules from 3mm to 70mm. Additionally, a clustering method is used to find a 3D candidate from 2D bounding boxes. After that, two Inception-v4 networks with different receptive fields are considered as a classifier for false positive reduction. The authors obtained a sensitivity of 90% with 15 false positives per scan and a CPM score of 90. 3% on LUNA16 dataset. In a more general approach, Tong et al. , 2020  [8]  proposed Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) for analyzing anchor box to Faster R-CNN in order to further improve candidate detection performance.
This is an extension of K-means clustering to automatically merge and split clusters during its iteration process, making it generally more adaptive than the original K-means method. This K-means-based anchor box clustering method achieves 91. 43% sensitivity at 3. 19 false positives per scan on LUNA16 dataset. A similar work by  Fan et al. , 2021 [25]  also proposes Local Density based Iterative Self-Organizing Data Analysis Techniques Algorithm (D-ISODATA) for configuring anchor boxes automatically. This work achieves 93. 6% sensitivity with 0. 15 false positive per scan. Although these methods have obtained satisfactory results, most of them use manually designed configurations or simple clustering methods to generate Faster R-CNN's anchors, which are rarely suitable with the variability of nodule sizes and shapes in CT images.
In this paper, our work improves nodule detection performance of Faster R-CNN by automatically learning its anchor box sizes from ground-truth nodule sizes in the training dataset. In the second phase, false positive reduction, Dou et al. , 2016  [26]  propose a multi-contextual 3D Convolutional Neural Networks (CNNs) based framework in automated pulmonary nodule candidate detection from CT images and achieved better performance compared to 2D CNNs. By taking full advantages of the 3D spatial information, proposed model achieved 82. 6% of CPM score in the false positive reduction on LUNA16. Monkam et al. , 2019 [27]  developed an method to discriminate between pulmonary micro-nodules and non nodules from CT images. The system consists Extreme learning machine (ELM) to integrate the five 3D-CNNs to efficiently detect pulmonary nodules from 3D CT image data.
The experiments result on LIDC-IDRI dataset got an accuracy of 97. 3%, Area Under Curve (AUC) of 98%, and sensitivity of 96. %5. Qin et al. , 2018 [28]  used 3D U-Net architecture as the backbone for region proposal network (RPN) to generate nodule candidates and 3D DenseNet architecture for false positive reduction. They employed multitask residual learning and online hard negative example mining strategy to accelerate training and improve detection accuracy. The proposed method achieved 96. 7% and 91. 7% for sensitivity and Competition Performance Metric (CPM) score on LUNA16 dataset, respectively. Li et al. , 2018 [29]  proposed an ensemble of convolutional neural networks (E-CNNs) that fuses of three different CNNs with different input sizes for detecting lung nodules. The achieved sensitivity on JSRT dataset is 84%.