dataesr/scientific-paragraphs-categorization huggingface dataset:
The high predictive performance of the LSTM model confirmed the potential utility of electrical muscle stimulation for the reduction of resting tremors in PD. Panyakaew and colleagues  [69]  investigated the identification of modifiable risk factors of falls. The input data consisted of clinical demographics, medications, and balanced confidence scaled by the 16-item Activities-Specific Balance Confidence (ABC-16) scale, from 305 PD patients (99 fallers, 58 recurrent fallers, and 148 non-fallers). They trained two gradient tree boosting algorithms using sevenfold cross-validation. They obtained good predictive performance at differentiating fallers from non-fallers, the most relevant features being item 7 (sweeping the floor), item 5 (reaching on tiptoes), and item 12 (walking in a crowded mall) from the ABC-16 scale, followed by disease stage and duration.
Acknowledgments The authors would like to thank  Jochen Klucken  for his fruitful remarks. This work was supported by the  French government  under the management of  Agence Nationale de la Recherche  as part of the " Investissements d'avenir" program , reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ) and reference  ANR-10-IAIHU-06  ( Agence Nationale de  la Recherche-10- IA Institut Hospitalo-Universitaire -6), by the  European Union   H2020 Programme  (grant number  826421 , project  TVB-Cloud ), and by the  ERA PerMed EU- wide project  DIGIPD  ( 01KU2110 ).
g. , The Australian  (Rinta-Kahila et al. , 2021) , Le Monde  (Rowe et al. , 2020) , The New York Times  (Fast and Horvitz, 2017 )), we started data collection using the national newspaper The Australian. We used the ProQuest database to retrieve and download articles during our designated period of analysis  (2015 -2022). Out of 805 articles, we identified relevant articles that either closely related to the discourse on regulation of digital platforms and algorithmic control, or captured broader societal discourse on the role of digital platforms in society, the development of algorithms, and the need for regulation. These articles can be seen as shaping the emotional and subjective positioning of the discourse. The research team engaged with the articles and compared our understanding of issues related to regulation of digital platforms, ultimately collecting 410 articles referring to the regulation context, of which 269 articles were chosen for detailed coding of two cases.
Policy and industry documents. We collected government policy documents and industry publications  (Ghazawneh and Henfridsson, 2012)  to gain insights into views and assumptions of the main participants in discourse on regulation, some of which were identified from the media articles. For case 1, for example, we collected all the documents that were part of the ACCC digital platform public inquiry, including submissions to the inquiry, concept notes and preliminary and final reports. This phase of data collection resulted in 483 documents for analysis (see Table  2 ).
Internationally governments set the framework for the GIFCT by foregrounding the development of AI-algorithms ("technological tools for automated detection and removal") to improve detection and removal of violent content ("within 1-2 hours of upload"), as illustrated by the following excerpt: We call upon Internet companies to enhance the potential of the Database of Hashing to share hashes of known terrorist content across companies. We underscore the challenge to industry and we urge it to work collaboratively through the Global Internet Forum, to develop solutions to identify and remove terrorist content within 1-2 hours of upload, to the extent it is technically feasible, without compromising human rights and fundamental freedoms. We also urge the companies to pursue the development of technological tools for automated detection and removal (G7 19/10/2017).
Patient selection and data collection Eligible patients were those presenting with previously treated histology-proven advanced NSCLC and who had received at least one cycle of either nivolumab, pembrolizumab, or atezolizumab as a single agent between January 2015 and December 2017 in the Léon Bérard Cancer Center (Lyon, France) and the comprehensive Georges-François Leclerc Cancer Center (Dijon, France). Patient data were collected after institutional review board approval. Patients not agreeing to the use of their clinical data for an academic study were excluded according to national and European laws. Clinical and pathological data were collected using electronic medical records. Clinical variables included sex, age at ICI initiation, and outcome-related data [progression-free survival (PFS) under ICI, overall survival (OS), radiological tumor response at 3 months (12 weeks), and best radiological response according to  RECIST 1.
1]. To build the models, patients were divided into two classes. The first class was made up of patients who showed complete response (CR), partial response (PR), or stable disease (SD) at 3 months and were considered patients with disease control (DC). The second class was made up of patients with progressive disease (PD) according to RECIST 1. 1 and/or clinical progression or death before 3 months. The patients included in the study underwent a CT scan with available DICOM images 1 month prior to the beginning of the treatment at most. The data cutoff date was February 2, 2019.
Transcriptomics In a 54-patient subgroup with formalin-fixed paraffinembedded samples, we retrieved targeted-RNA sequencing data previously reported by our group (GSE161537)  (19, 20). Each tumor was classified as HOT or COLD based on a 27-gene expression signature. HOT tumors were shown to be characterized by an overall activated immune microenvironment by (i)-PD-L1 and IDO1 expression, (ii)-TCD8 infiltrate, and (iii) activation of the IFN-gamma pathway. Among the 54 patients, 31 and 23 patients had tumors classified as HOT or COLD, respectively.
Radiomic feature extraction Patients underwent CT scans using various systems [Siemens (n = 63), Philips (n = 25), General Electric (n = 85), Toshiba (n = 9), Hitachi (n = 2)] with various protocols (voltage range: 100-130 kV, X-ray-tube current: 350-700 mAs, pitch: 0. 8-1. 5). Images were reconstructed using a soft kernel for all patients [range of image thickness (1-3 mm)]. Images were automatically loaded on an in-house software developed on MATLAB R2019a (The Mathworks, Natick, MA, USA). The tumor was manually segmented in three dimensions by an experienced radiologist (AB, nine years of experience in oncology imaging), and the data were blinded for clinical results. Tumor segmentation was performed slice by slice to generate the tumor mask using ITK-SNAP (www. itksnap. org). The radiologist defined the contours of the tumor on the soft-kernel reconstruction images (Figure  1 ).
Feature selection After the extraction of radiomic features, each database was separately normalized using the Z-score. An initial step of dimensionality reduction was performed (Figures  3, 4 ). Two different approaches were tested. In the first approach, feature selection was performed using the ReliefF algorithm, with k = 10 being the nearest neighbor. In the second approach, we used a statistical method accounting for relevancy and redundancy. The method ranks the features by computing a score combining the results of a statistical test Z (for relevancy) and correlation information to outweigh the Z-value of potential features (for redundancy) using S ¼ Z Â [(1 À a) r] where ρ is the average absolute value of the cross-correlation coefficient between the candidate feature and all previously selected features; α is the The number of features integrated into the model was adjusted to the size of the data so that it was consistent with the number of observations.
Predictive model training Various models were trained with different databases, different outcomes, and different combinations of feature selection methods and classifiers. In each case, we performed a binary classification (DC vs. PD or HOT vs. COLD). We compared two different classifiers for each model (convolutional neural network (CNN) vs. SVM). For the model trained on radiomic data, since the number of patients was more than 100, we used an artificial neural network with a feed-forward multilayer perceptron architecture. For the three other models (trained on radiogenomic data), the number of patients was smaller, and we used random forests with a split of 10. For every model, we also used a support vector machine trained with a linear kernel and box constraints set to one as a second classifier.
The following predictive models were built. Radiomic analysis pipeline for the prediction model of the 3-month PFS, using the Lyon cohort for training and the Dijon cohort for validation. During the segmentation step, the radiologist defined the contours of the tumor, selecting all the tissue parts and excluding peripheral vessels or non-tumoral lung condensation. In total, 342 radiomic features were automatically extracted. Each database was separately normalized using the z-score. The training set was used to build the model. Dimension reduction was performed using one of two feature-selection methods (t-test or ReliefF). Then, machine learning was performed using two different classifiers: an SVM or an artificial neural network with feed-forward (FF) multilayer perceptron architecture. Internal validation was systematically performed to evaluate overfitting, using a hold-out cross-validation technique, with 75% of the database used for training and 25% for validation.
Prediction of PFS at 3 months based on radiomics To build this model, two classes were considered. The first class constituted patients who showed CR, PR, or SD at 3 months and were considered patients with DC. The second class constituted patients with PD according to RECIST 1. 1 and/or clinical progression or death before 3 months. The training database was built using the patients treated in Lyon [n = 110 patients (60 DC vs. 50 PD)]. The number of selected features after feature reduction was set at n = 30. To evaluate overfitting, a hold-out cross-validation technique was performed with 75% of the database used for training and 25% for validation. Next, the model inference was performed separately on the Dijon database used as an external validation set [n = 85 patients (61 DC and 24 PD)].
Prediction of PFS at 3 months based on radiomics and genomics Since the number of patients who had both radiomic data and genomic data was lower than in the previous step, 51 patients treated in Lyon and three patients treated in Dijon were merged into a single cohort (39 DC and 15 PD). The population was dichotomized into two classes of DC and PD as previously. For each patient, the 342 radiomic features and 2,559 oncologyrelated biomarker genes were merged into a single database. The number of selected features after dimension reduction was set at n = 20. To evaluate overfitting, a hold-out cross-validation technique was performed with 75% of the database used for training and 25% for validation (Figure  4 ).
Prediction of the HOT/COLD status using radiomics To build this model, the 54 patients who had both radiomic and genomic data available were included. The population was dichotomized into two classes of HOT status and COLD status, as previously explained. For each patient, the 342 radiomic features were included in the database. The number of selected features after dimension reduction was set at n = 15. To evaluate overfitting, a hold-out cross-validation technique was performed with 75% of the database used for training and 25% for validation (Figure  4 ).
Prediction of PFS at 3 months based on radiomics and HOT/COLD status To build this model, the 54 patients who had both radiomic data and HOT/COLD status were included. The population was dichotomized into two classes of DC (n = 33) and PD (n = 21). For each patient, the 342 radiomic features and the HOT/COLD status were merged into a single 343-feature database. The number of selected features after dimension reduction was set at n = 15 (including the HOT/COLD statuses). To evaluate overfitting, a hold-out cross-validation technique was performed with 75% of the database used for training and 25% for validation (Figure  4 ).
subgroup of patients, but the HOT/COLD status was demonstrated to be predictive of a better response to treatment than in our previous study  (19) , which included all patients. The transformation of the complete genomic database of 2,559 genes into one single phenotype can be compared with selecting the most relevant features, and this contributes to limiting the risk of overfitting. However, with regard to some of the genes selected in our models, we may assume that the feature selection method is relevant and is able to capture genomic signatures together with radiomic features. Indeed, using the ReliefF methods-among the total of 20 features, chosen to be included in the models-four were radiomic features and 16 were genomic features. That the genomic features were overrepresented was to be expected because the algorithm has to select features among 2,559 genes and 342 radiomic features.
Second, a retrospective study such as ours implies highly varying imaging protocols. Statistical harmonization methods such as ComBat  (35, 36)  could be useful in this context to address potential batch effects linked to acquisition protocol heterogeneities. In order to perform a batch correction, other studies have to be done to select the batch effect criterion. On the other hand, the model might well learn acquisition protocol heterogeneity. When the bias/variance dilemma is well-balanced, the classifier learns a general law rather than dataset specificities. Another limitation of our study pertains to the manual segmentation of the lesion. Reproducibility and repeatability could not be tested in this study because tumors were segmented by a single radiologist. Indeed, the segmentation of grade III/IV patients' lesions is challenging because of their varying localization, shape, and margins  (37).
In low precipitation environments characterized by arid and semi-arid climates that dominate Morocco, soils are generally poor in organic matter. Thus, knowledge of the evolution of SOC is essential because it can allow management measures to be adapted according to needs, particularly in the region's vulnerable ecosystems. The aim of this study is to predict SOC, BD, and SOCS at different soil depths (0-10 cm, 0-20 cm, and 0-30 cm) in the Ourika watershed in Morocco using ML models. Moreover, we look to identify the most important explanatory factors of SOCS in the watershed and to test if incorporating or excluding SoilGrids and WorldClim data as covariates would influence model accuracy. The objective is to provide spatial data on SOC that can serve as a resource for sustainable soil management in forests and agroecosystems.
Vegetation and land use influence SOCS dynamics through the depth distribution of organic carbon, which varies among different plant functional types due to different carbon allocation patterns  [54]. As for soil type, texture, and structure, they represent one of the driving factors influencing SOCS  [51, 55, 56]. Indeed, soil structure is enhanced by organic matter by improving physical properties through different organic binders  [50]. The interaction between soil mineralogy and SOCS has been the subject of much research, with clay and silt found to influence SOC stabilization, thereby controlling soil carbon sequestration  [57]. Data used for this study were obtained from various sources. Chemical and physical property variables were collected from SoilGrids (https://soilgrids. org/ (accessed on 4 May 2022):  [58] ), while climate predictors were downloaded from WordClim (https://www.
worldclim. org/ (accessed on 4 May 2022):  [59] ). Topographic variables were extracted from a 30 m digital elevation model (DEM) obtained from the ASTER GDEM platform (https://asterweb. jpl. nasa. gov/gdem. asp, accessed on 18 May 2022), while remote sensing predictors were obtained from Landsat 8 images downloaded from the USGS GLOVIS platform (https://glovis. usgs. gov/app, accessed on 18 May 2022). In order to integrate the different variables in the different modeling steps of SOCS, it was necessary to extract the data related to the study watershed. Accordingly, this was performed in an R environment, and the respective variables and predictors are shown in Figures  A1-A4. Soil variables (Figure  A1 ) included BD, cation exchange capacity at neutral pH, carbon density, coarse fragments, clay, silt, nitrogen, and sand contents, each at different depths (0-5 cm, 5-15 cm, 15-30 cm) and soil types.
Regression Algorithms 2. 4. 1. Random Forest (RF) Developed by Breiman  [60] , RF is composed of several decision trees, working independently on a view of a problem. The efficiency of RF models strongly depends on the quality of the initial data sample. RF is based on the bagging principle. A dataset is initially split into smaller groups (decision trees), after which a training model is set up for each group. The output of these decision trees is then aggregated to produce the most reliable prediction.
V. inaequalis is an hemi biotrophic pathogens which colonizes only the apoplast compartment since the beginning of the interaction. SMT2 strong relative induction in our data could indicate that a similar mechanism of nutrient efflux regulation via sterols could take place to limit the fungus growth in pear nonhost resistance against V. inaequalis. In our data, the other DEGs that were linked to the secondary metabolism belong to the phenylpropanoid pathway production (Fig. 6 ). Among them we found four genes belonging to the flavonoid production, all repressed, at 24hpi (DFR and DRM6) or 72hpi (TT7 and UGT71D1). DFR (dihydroflavonol reductase) is involved in flavan-3,4-ol production and TT7 (flavonoid 3' hydroxylase) in dihydroquercetin production from dihydro-kaempferol, and UGT71D1 (glucosyl transferase) in quercetin-glycoside production from quercetin (TAIR database; https://www.
inaequalis: simple coumarin on one hand and hydroxycinnamic acid amides on the other hand. We found four BGLU-like genes induced at 24hpi: BGLU42 (also induced at 72hpi), 47 and BGLC3, or 72hpi: BGLU16 (Fig. 6 ). These β-glucosidases could be implied in simple coumarin path production from the cinnamic acid (KEGG database). Some natural simple coumarins are known as antifungal compounds in vitro and have been developed as fungicides  [92]. Ancient work on Hevea also reports the correlation between the resistance against pathogenic fungi and the production of some coumarins, with antifungal activity in vitro  [93]. We also found induced at 24hpi the genes AACT1/ACT1, ATPAO5 and genes homologs to At4g17830 and At4g38220 (Fig. 6 ). AACT1/ACT1 catalyze the first specific step in branch pathway synthesizing hydroxycinnamic acid amides from the p-Coumaroyl CoA or the feruloyl CoA and amines agmatine or putrescine  [94].
Hydroxycinnamic acid amides are produced in response to pathogenic infections  [94]  and surface exported. Hydroxycinnamic acid amides are reported to participate in Arabidopsis nonhost resistance against Phytophthora infestans via their inhibitory activity on spore germination  [95]. The three others genes belong to the arginine biosynthesis path (homologs to At4g1783 and At4g38220) and the arginine and proline metabolisms which produce the amines agmatine and putrescine (ATPAO5) (KEGG database). Agmatine is directly produced from arginine thanks to an ADC activity (arginine decarboxylase) and putrescine can be produced from spermidine thanks to a PAO activity (polyamine oxidase). ATPAO5 catalyzes the conversion of spermine in spermidine. The induction of these three last genes is therefore consistent with the hypothesis of amines production in order to enable hydroxycinnamic acid amides synthesis.
The induction of C4H at 24hpi could also favor hydroxycinnamic acid amides synthesis via p-Coumaroyl CoA biosynthesis promotion. C4H (cinnamate 4-hydroxylase) catalyzes the production of p-Coumaric acid from Cinnamic acid and p-Coumaric acid gives p-Coumaroyl CoA thanks to 4CL (4-coumarate-CoA ligase) (KEGG database). Among the suite of defense components synthetized in nonhost as in host context, a chemical barrier can be established via accumulation of a diverse array of secondary metabolites rapidly produced upon pathogen infection, named phytoalexins, with toxic or inhibitory effects (reviewed in [10]). Phytoalexins can be flavonoids, such as the pisatin of pea (in  [96] ) but also varied phenylpropanoid compounds. In the nonhost interaction pear / V. inaequalis, the production of flavonoid type phytoalexins does not seem to be favored, except simple coumarin and hydroxicinnamic acid amines.
in the cell wall  [98]. The repression of this gene is thus in favor of more OH. in the cell wall. The oxidoreductase coding gene AOR is known in the chloroplast to contribute to the detoxification of reactive carbonyls produced under oxidative stress  [99]. NRAMP genes function as positive regulators of ROS accumulation, especially during Arabidopsis Erwinia chrisanthemi resistance [100]. The induction (at 24 and 72hpi) of another gene suggests modifications at the cell wall level: EXP8, an expansin coding gene involved in cell wall loosening (Tair database). We also found two genes related to hormone pathways, one induced at 24hpi: WIN1 and the other one repressed at 72hpi: UBP12. WIN1 is known as a negative regulator of SA pathway [101] and UBP12 as a positive regulator of JA pathway via the stabilization of MYC2  [16].
QPCR validation of transcriptomic data In order to validate transcriptomic data, QPCR was performed on a selection of gene/sample associations. First-strand cDNA was synthesized using total RNA (2. 0 μg) in a volume of 30 μl of 5× buffer, 0. 5 μg of oligodT15 primer, 5 μl of dNTPs (2. 5 mM each), and 150 units of MMLV RTase (Promega, Madison, WI, USA). The mixture was incubated at 42°C for 75 min. Quantitative RT-PCR (QPCR) was then performed. Briefly, 2. 5 µl of the appropriately diluted samples were mixed with 5 µl of PerfeCTa SYBR Green SuperMix for iQ kit (Quantabio, Beverly, MA, USA) and 0. 2 or 0. 6 µl of each primer (10 µM) in a final volume of 10 µl. Primers were designed with Primer3Plus, their volumes were according to their optimal concentration (determined for reaction efficiency near to 100%; calculated as the slope of a standard dilution curve; [134]).
CYP98A3 encode a C3H (coumarate 3-hydroxylase), CAD9 encode a CAD (cinnamyl alcohol dehydrogenase), HCT is an hydroxycinnamoyl-CoA shikimate/quinate hydroxycinnamoyl transferase, CCR1 encode a CCR (cinnamoyl-CoA reductase) and At2g23910 encode a CCR-related protein. (TAIR and KEGG databases (https://www. genome. jp/kegg/)).
Metadata for the 172 (162 for pear and 10 for apple) DEGs discussed in this work are available in TableS2and S3 (Online only). PCP017051 for pear, available at https://www. rosaceae. org/, with the datasets on "Pyrus communis v1. 0 draft genome") were used to check the absence of genomic DNA contamination by PCR. The PCR reaction conditions were as follows: 95°C for 5 min, followed by 35 cycles at 95°C for 30 s, 60°C for 45 s, 72°C for 1 min, with a final extension at 72°C for 5 min. The PCR products were separated on a 2% agarose gel. Amplifications (aRNAs) were produced with MessageAmpII aRNA Kit (Ambion Invitrogen, Waltham, MA, USA), from 300 ng total RNA. Then 5 µg of each aRNA were retrotranscribed and labelled using a SuperScript II reverse transcriptase (Transcriptase inverse SuperScript™ II kit, Invitrogen, Carlsbad, Purity and concentration of the samples were assayed with a Nanodrop spectrophotometer ND-1000 (ThermoFisher Scientific, Waltham, MA, USA) and by visualization on agarose gel (1% (weight/volume) agarose, TAE 0.