dataesr/scientific-paragraphs-categorization huggingface dataset:
Study limitations The implemented method for SPECT registration to a reference CT image or planar registration to a projected SPECT has been tested thanks to a single phantom experiment with one radionuclide. However, the implemented algorithm is rather simple since it relies on manual registration. Further tests in more complex situations (e. g. , when background activity is present) might give insights on the limitation of the method but would not question the implemented feature itself. Currently, the activity assessment in OEDIPE relies on the calibration factors provided by the user or included in the DICOM images. No special module is devoted to their assessment. Similarly, OEDIPE processes images in DICOM format and thus depends on the reconstruction parameters implemented in the imaging device.
Usefulness of Monte-Carlo calculations To facilitate comparison with known data, we aimed first at addressing simple experimental and clinical conditions related to radioiodine therapy. For such a short-range emitter, full Monte-Carlo calculations are not strictly necessary. Still, the MCNPX calculation time to assess mean doses in the patient case was 65 mins, which is compatible with clinical applications. OEDIPE software can be run on a regular PC or laptop. In this work, MCNPX calculations were run on a dedicated cluster. Each calculation was launched on a single node, characterized by 2 CPU of 8 cores each and a total RAM of 32 GB. When short range emitters are used and mean doses needed, DVK methods, or calculations with specific absorbed fractions, are certainly sufficient. However, when tissue heterogeneities are present, or when emitters have penetrating radiations in their spectrum (e.
A workflow for patient-specific dosimetry Despite the limitations discussed above OEDIPE integrates the essential steps needed to assess patient specific dosimetry. All images used in nuclear medicine can be handled including combination of SPECT/CT and planar images, in that case the registration and quantification can be performed on any of the SPECT/CT image. Calibration factors can be entered by the user, they can take into account dead time correction, or any correction effect in fact. Moreover, recovery factor can be taken into account independently of this correction factor and all quantification is performed at the VOI level, enabling to use VOI-specific quantification. The biokinetic module integrates several predefined fit functions and if they were not suitable for the user data could be fitted with other programs.
Conclusion To account for the patient-specific biokinetics, after administration of radiopharmaceuticals, in the estimation of absorbed doses to lesions and healthy tissues, new features were implemented in OEDIPE. These developments enable an automatic analysis of multiple SPECT images or hybrid planar/SPECT images, and to derive the cumulated activity for each VOI. The implemented functionalities have been validated using images acquired with a Jaszczak phantom for both approaches. OEDIPE can now handle 3D Monte Carlo personalized dosimetry including patient-specific anatomy and biokinetics for 246 radionuclides included in the ICRP database. In this work, the implemented features have been applied to a clinical case. When mean absorbed doses are needed, for example to establish dose effect-relationship of small tumours, OEDIPE could be used as a complementary and free tool in clinical trials.
For the representation of surface fluxes and exchanges with the atmosphere, AROME model is coupled to the surface modelling platform SURFEX (Externalized Surface)  (Masson et al. , 2013). In NWP models, the assimilation of satellite data requires the knowledge of the atmospheric state and an accurate description of surface conditions, in particular of surface parameters such as surface temperature and emissivity. These surface parameters are used in the simulation of the model equivalent of the satellite radiances. However surface models can present deficiencies and a possible approach, used at Météo-France, to obtain realistic surface conditions in the radiance simulation, consists in retrieving one of these parameters for each sensor using a channel located in an atmospheric window. This retrieved temperature or emissivity is then used for the simulation and the assimilation of the other channels of the instrument in the AROME model.
The paper is arranged as follows. First, we introduce the AROME atmospheric model and its data assimilation system together with the LST retrieval method used for infrared SEVIRI radiances. In section 3, we present the SURFEX modelling platform with its surface analysis scheme (  (Giard and Bazile, 2000) ,  (Mahfouf et al. , 2009)  and  (Masson, 2000) ). Modifications necessary to handle SEVIRI LST in the soil analysis scheme  (Noilhan and Mahfouf, 1996)  are described in section 4. Then, the experimental framework of cycled analyses and forecasts over a two-month summer period with the AROME NWP system is presented in section 5, together with objective evaluations against observations. Finally, conclusions and further steps are discussed in section 6.
, 2006; Guedj et al. , 2011)  have shown the interest of using the retrieved surface parameters for the satellite radiances data assimilation. At Météo-France, the retrieval of surface temperature is performed for infrared instruments assimilation (SEVIRI and IASI) in clear sky. On the contrary, the retrieved surface emissivity is used to assimilate the microwave instruments knowing the larger variation of surface emissivity in the microwave bandwidth. The objective of the study is to assimilate the retrieved land surface temperature from SEVIRI in the land surface model of AROME model. Sassi et al. (2019)  have shown that the representation of the diurnal cycle with satellite derived land surface temperature is more accurate than the representation of the model, especially in spring and summer.
A CASE STUDY: LA LOIRE BASIN SUB-DOMAIN For a first experimental set-up, we chose a summer period covering July and August 2019 to assimilate clear sky SEVIRI LST to optimize the number of clear sky data. We rely on the NWC SAF (Le Gleau, 2019) cloud classification provided with the SEVIRI data to select pixels classified as "clear over land". A spatial thinning is also applied with one pixel retained out of five. This corresponds to the operational thinning ratio used in the atmospheric analysis. Finally only nighttime pixels are assimilated since the observation error standard deviations are rather low by nighttime, as demonstrated in the previous section, and as previous studies showed that LSTs retrieved from different infrared sensors agree better during nighttime  (Sassi et al. , 2019).
On one hand, at the level of LST data itself, the retrieval methodology from infrared instruments using a fixed emissivity can be improved. We have considered during this work an LST retrieved from SEVIRI only using the operational mono channel retrieval method and a surface emissivity atlas as used in operations. However, we note that the emissivity atlas used for SEVIRI LST retrieval is different from the emissivity atlas used for other infrared sensors. In this direction, it would be worth to investigate the impact of SEVIRI LST assimilation while using a common emissivity atlas for all infrared sensors. To go further towards infrared sensor synergy, we can investigate the use of a different retrieval method that allows a simultaneous retrieval of LST and surface emissivity over land. It would be interesting also to evaluate the benefit of assimilating LST retrieved from different sensors in the infrared, together with surface information on surface temperature or moisture provided by channels in the microwave bandwidths.
We tested seven classification algorithms using 5-fold cross-validation, with 80% of the data being randomly selected for training and the remaining 20% for testing. We assessed model performance using accuracy, precision, recall, F1-score, and AUC score. We used an autoencoder to perform dimensionality reduction prior to classification. We observed consistently better performance using logistic regression and linear discriminant analysis models when compared to the other models we tested. Classification performance generally improved when an autoencoder was used, except for when we predicted the need for adjuvant treatment. We obtained overall best results using a logistic regression model without autoencoding to predict the need for adjuvant treatment (F1-score = 0. 869).
The recently developed Adjutorium (https://vanderschaar-lab. com/adjutorium/) is a breast cancer prognostication and treatment benefit prediction model that outperforms PREDICT  [19]. Adjutorium used large-scale publicly available datasets from the United Kingdom and the United States consisting primarily of patients aged 30-65 years, along with a smaller subset of older patients (age > 65 at diagnosis). Due to limitations of the datasets, Adjutorium did not include important tumor information such as progesterone receptor (PR) status. In this study, we generate models that address both these limitations. For one, our cohorts are highly representative of elderly breast cancer patients because they include only patients aged 70 years or more. In addition, we make use of an extensive dataset that includes administrative, biological, treatment, primary tumor, and survival data.
We used software ConSore to build our database. ConSore is a data mining tool developed by UNICANCER, a French academic cancer research organization  [22]. Natural Language Processing is used to select patient cohorts and extract data from electronic medical records (EMR), providing a homogenous collection of meaningful information. Information is extracted in a structured form according to research criteria. It should be noted that a second human check was nevertheless carried out on all EMR studied. Data collected in our analysis included the following patient characteristics at early breast cancer diagnosis: age, Eastern Cooperative Oncology Group performance status, body mass index (BMI), comorbidities (diabetes, cardiac insufficiency, coronary insufficiency, chronic obstructive pulmonary disease (COPD) and cognitive disorders), hospitalization history in the previous year, polypharmacy (> or = 5 medications a day).
The following biologic measures at diagnosis were collected: hemoglobin, lymphocytes count, albuminemia, creatinine clearance (Cockcroft-Gault). The following data about disease characteristics were extracted: histological subtype, hormone receptor status, Human Epidermal Growth Factor Receptor-2 (HER2) status, Scarff-Bloom and Richardson (SBR) grade, number of tumors, size of the biggest tumor, and lymph node involvement according to the TNM classification  [23]. The expression of estrogen receptors (ER), progesterone receptors (PR), and HER2 status were issued from the histopathological results of pre-therapeutic biopsy. Hormone receptor negativity was defined if less than 10% of cells stained for estrogen and progesterone receptors. The expression of HER2 was considered negative in case of lower than 1+ immunohistochemistry staining.
For tumors with a score of 2+, an additional in situ hybridization determined HER2 amplification or non-amplification  [24]. Data about cancer treatments included: type of surgery, lymph node dissection, adjuvant radiotherapy, adjuvant chemotherapy, adjuvant HER2 targeted therapy, adjuvant endocrine therapy. Continuous variables were categorized based on expert opinion while categorical variables with more than two categories were dichotomized by creating a binary column for each category (S1 Table ). The present analysis received approval from the French Data Protection Authority (Commission Nationale de l'Informatique et des Libertes, authorization no 9191415; October 10, 2019) and was built in compliance with French and European regulations.
Statistical analysis Autoencoder. To adequately model a high-dimensional dataset, it is advantageous to perform dimensionality reduction prior to classification. Autoencoders are an efficient dimensionality reduction technique  [25]. An autoencoder is a type of artificial neural network that learns a representation of the dataset while ignoring noise in the data; it can compress existing and missing information together, without the need for removing or imputing missing values. In this study, we used a classical autoencoder with one encoding function and one decoding function, and a binary cross entropy loss function. To assess the difference in performance due to the use of an autoencoder, we performed two sets of analyses. In the first one, we performed dimensionality reduction of our data using an autoencoder and subsequently generated predictive models.
In the other, the autoencoder was not used prior to data modeling. Algorithms and performance measures. Predictive modeling is a branch of machine learning that uses data mining to predict results. In this study, we tested seven classification algorithms, and compared their performance in predicting discrete outcomes of interest. We used the Random Forest, Decision Tree, Naïve Bayesian, Linear Discriminant Analysis, Logistic Regression, Nearest Shrunken Centroids, and Neural Networks algorithms. We performed all analyses in Python programming language [Python Software Foundation, https://www. python. org] using sklearn  [26]  and imblearn  [27]  libraries with default parameter values. We used 5-fold cross-validation, with 80% of the data being randomly selected for training and the remaining 20% for testing.
We assessed model performance using accuracy, precision, recall, F1-score, and AUC score. We report averaged performance values over the five executions. Due to the imbalanced nature of our dataset, we selected our best-performing models based on the F1-score. Predicted outcomes. Overall survival is widely accepted as the gold-standard primary endpoint. Because our goal was to develop a decision aid to assist clinicians in the choice of post-surgery treatment for elderly breast cancer patients, we focused on 5-year overall survival. Our approach was to generate outcome predictions for different courses of action using data from our cohort. The first outcome of interest was all-cause mortality at 5 years, where the objective was to predict whether a patient will die within five years from the date of surgery.
To further assess the impact of the autoencoding step in model performance, we analyzed the information loss associated with the use of an autoencoder. Autoencoders generate a representation of a given input dataset using fewer dimensions that the original dataset. The encoding process of an autoencoder may lead to information loss. Information loss is the increase in entropy by transforming a dataset and is calculated by comparing the entropy of the dataset before and after a transformation. In this study, we used an autoencoder to compress the input dataset before each predictive task. In other words, we generated a lower-dimensional dataset to predict death within five years, another lower-dimensional data to predict the need for adjuvant treatment, and so on. To quantify the information loss of the autoencoding step, we compared the entropy of the dataset before and after the transformation.
We used the following equation to compute entropy, where p(x) denotes the probability of each possible outcome  [28] : Entropy ¼ À X pðxÞlogðpðxÞÞ In Table  6 , we report the average value and standard deviation of the information loss across 100 runs of the autoencoding step. In this analysis, a positive value indicates that the entropy of the compressed dataset is larger than that of the original dataset. Interestingly, we obtained overall best results using a logistic regression model without autoencoding to predict the need for adjuvant treatment (F1-score = 0. 869). To contribute with clinical insight, we performed a feature importance analysis to identify the most predictive features for each outcome of interest. Feature importance techniques assign a score to input features of a predictive model that indicates the relative importance of each feature when making a prediction.
Inspecting the importance scores provides information about which features are the most and least important for the model when making a prediction. Linear algorithms, such as linear discriminant analysis and logistic regression, fit a model where the predicted output is the weighted sum of the input values. These algorithms determine the set of coefficients to be used in the weighted sum in order to make a prediction. If we ensure that the input variables have the same scale or have been scaled prior to fitting the model, we can use the resulting coefficients directly as a type of feature importance score. In this work, we used library sklearn from Python to retrieve the property coeff_ that contains the coefficients for each input variable of the linear discriminant analysis and logistic regression models.
We then ranked all coefficients in decreasing order and retained the five highest ranked coefficients for each model. Decision tree algorithms and ensembles of decision trees, such as random forest, offer importance scores based on the reduction in the criterion used to select split points, like Gini or entropy. After fitting the models using library sklearn in Python, we retrieved property fea-ture_importances_ that contains the relative importance score for each input feature of the decision tree and random forest models. We then ranked all scores in decreasing order and retained the five highest ranked scores for each model. Results from this analysis are shown in Tables  7 8 9 10. The entries (numbers 1 thru 5) indicate the rank of each feature for each algorithm. For example, in Table  7  feature 'Post-operative radiotherapy' was ranked as the second most predictive feature in the Random Forest model and fifth most predictive feature in the Decision Tree model.
869). We acknowledge limitations of our study, such as the imbalanced nature of our dataset, meaning that not all response classes included similar numbers of patients. Additional work could analyze the effect of class imbalance on model performance. Moreover, in this work we did not stratify patients based on, e. g. , hormone receptor status or clinical staging prior to modeling. In the future, additional models could be generated for specific patient subgroups. Hence, external model validation could be performed for general and subgroup-specific models. We also acknowledge limitations in our dichotomous definition of outcome, where we consider that a choice is correct if the patient survives at least five years. Future work could consider a multi-factorial outcomes definition, by defining, e.
, 2013). The aerosol characterization was performed through the use of a set of vertically resolved and columnar-integrated observations and the GRASP algorithm was applied to those measurements carried out at eight sites distributed over the IP and integrated into the different mentioned networks (EARLINET, ICENET and MPLNET). The paper is organised as follows. Section 2 gives a description of the methods and data used for the VA characterization. In section 3 the results are presented and discussed, and in section 4 the conclusions are given.
Satellite data The monitorization of volcanic ash plumes from space was done with the brightness temperature difference (BTD) method  (Prata, 1989) , based on the increase of absorption by ashes in the range of 10-13 μm. To this aim, 1-km resolution calibrated radiances from MODIS/Aqua collection 6. 1 level 1B data from NASA were used. Concretely, brightness temperature at 11 μm minus brightness temperature at 12 μm was retrieved from channels #32 (11. 030 μm) and #31 (12. 020 μm), respectively, using the SNAP (Sentinel Application Platform, https://st ep. esa. int/main/download/snap-download/) tool, developed by the European Space Agency (ESA). The method relies on identifying pixels affected by volcanic ash by the use of negative BTD (larger absorption at 11 than at 12 μm), after contextual filtering of false positives, which might be present over certain scenarios such as soils with high quartz content, ice-covered surfaces, high altitude clouds with low cloud-top temperatures and prevalence of clear sky situations with negligible atmospheric absorption  (Picchiani et al.
, 2012)  were used to detect and track the transport of the volcanic plumes over the IP. TROPOMI is a spectrometer that acquires data at four different spectral regions (ultraviolet, visible, near-infrared and shortwave infrared) providing, with a high spatial resolution of 7 × 7 km 2 , daily global information on trace gases and aerosols, important for air quality monitoring and climate studies  (Veefkind et al. , 2012). The data used correspond to TROPOMI Level 2 SO 2 data product. These data were downloaded from the European Space Agency (ESA) Sentinel-5P Pre-Operations Data Hub (https://s5phub. copernicus. eu/; last access on 5 August 2022). The SO 2 total vertical column analysis was carried out by using the HARP toolkit (Python interface) of the Atmospheric Toolbox (available at https://atmospherictoolbox.
, 2019). For the MPL and ceilometer inversions combined with sunphotometer, the values of AOD and sky radiances used were obtained from CAELIS  (Fuertes et al. , 2018; González et al. , 2020)  instead of AERONET. CAELIS is a software tool developed by the Group of Atmospheric Optics at the University of Valladolid (GOA-UVa), to manage data from the Iberian Network for Photometric Measurement of Aerosols and to investigate aerosol properties over Europe. Cloud-screened AOD and sky radiances from both AERONET and CAELIS databases are similar as demonstrated in  González et al. (2020).
In fact, the retrievals based on ceilometer data were automatically calculated by CAECENET  (Román et al. , 2021) , which is an automatic system that inverts data from ICENET (ceilometer) and CAELIS (sun-photometer) with GRASP (method of  Román et al. , 2018)  in near-real-time  (Bazo et al. , 2023). The use of products already processed from CAECENET is the main reason for the use of CAELIS data in the single-wavelength lidar inversions instead of AERONET ones. The Cerro-Poyos sun-photometer dataset was combined with the ceilometer installed at the Granada site which is about 12 km away (horizontally). The GRASP output variables (vertically-resolved and column integrated) used in this work are the total volume concentration, the total backscatter and extinction coefficients, aerosol optical depth, Ångström exponent, volume size distributions, and single scattering albedo.
Comparing the different sites, Cerro-Poyos and Granada presented very similar layers that extend from about 3-5 km asl. This similarity is expected due to the proximity of the stations (the ceilometer data used in the retrieval is the same). Despite that, Cerro-Poyos presents higher VC in the layer centre than in Granada. Still, on 25 September, a more extensive layer (1. 5-4. 2 km asl) was observed in Barcelona station with a maximum VC around 3 km agl. This agrees with the Hysplit backward-trajectories ending in Barcelona at 08:00 UTC (figure not shown) that presented similar patterns at all simulated heights (1-5 km with steps of 0. 5 km) to the volcanic aerosol plume observed by TROPOMI (Fig. 3 ). On 28 September the situation changed, volcanic aerosol layers were observed at lower altitudes (1.
Acknowledgements The authors acknowledge the use of GRASP inversion algorithm software (http://www. grasp-open. com) in this work. The authors also acknowledge the virtual mobility grant ( E-COST-GRANT-CA18235-508df2c9 ) funded by the  PROBE Cost Action. C. Córdoba-Jabonero ,  M. Á. López-Cayuela  and  C. V. Carvajal-Pérez  thank the PIs of the  AER-ONET  and  MPLNET El Arenosillo/Huelva  site and its technical staff for maintenance support. The  MPLNET  project is funded by the  NASA   Radiation Sciences Program and Earth Observing System.