dataesr/scientific-paragraphs-categorization huggingface dataset:
These five algorithms are applied to the dataset presented in Section 5. 1 (cf. Figure  3 ); the feature space where the algorithms operate is the twelve-dimensional space spanned by the twelve sensors. Their respective results are represented in Figure  9 , where every method is performed twice (corresponding to the two graphs of a same row), each time with a different value set to the main parameter. The main parameters are the objective number of clusters for the three first algorithms (K for the K-Means, and the grid size for the SOMs and BSOMs), the maximal distance ε delineating the neighborhood of a data for DBSCAN, and the minimal number of samples a cluster must comprise for OPTICS. Notice that this very last parameter can apply to every method, but it was chosen specifically for OPTICS since it has no true other parameter to vary to affect the results.
As such, the initial objective of the proposed methodology has been correctly achieved, i. e. , overcoming the necessity of setting that specific parameter before processing without having to use non-efficient and computationally heavy methods, such as the Elbow method or its silhouettes-based variant. Furthermore, even though the SPRADA method is very generic and should work with any dataset, it was applied to real industrial data, provided by Solvay, one of the industrial partners involved in the European project HyperCOG, and proved able to correctly and accurately identify the major regions of the feature space spanned by the basis consisting of the different sensors of the processes under study, in particular the steady and shutdown states (thus, the two extreme states). This knowledge is greatly valuable in the context of predictive maintenance and anomaly detection, forecasting and diagnosis.
First, it has been assessed on only one dataset: it should be worth testing it on other ones, and not limited to the chemical industry. Second, the impact of the parameters has been greatly diminished, and the method proved to be resilient to most of them, resulting in very close partitionings, but there is one which still needs to be set, as of the threshold ε; it is dynamic, and the exact value does not really matter, for the methodology is resilient to it, but it still has to be set nonetheless, and will condition the grain of the partitioning. Third, the SPRADA method is well-suited to the identification of large and compact groups of data (the regular states), but not really to the isolation of smaller groups, such as, for instance, the transient states or the anomalies (even though this can nevertheless be performed by adjusting the coefficient ε).
Funding: This paper took place within the  Hyperconnected architecture for highly cognitive production plants  (HyperCOG) project. The project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  869886. Website: www. hypercog. eu (accessed on 26 July 2023). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Algorithm 1. 1 Pseudo-code of the SPRADA's recursive decomposition principle Input : A database D to recursively split and a split percentage ε. Output : The set C of the recursively split clusters. /* Function split_clusters operates the recursive decomposition */ 1 Function split_clusters(C local ) 2 for C ∈ C local do 3 if the Hyper-Density score of C is higher than ρ split then 4 Add C to C 5 else 6 C tmp ← Split cluster C // K-Means, SOM, BSOM 7 split_clusters(C tmp ) // Recursive decomposition 8 end 9 end 10 End /* General principle of the recursive decomposition */ 11 Begin /* Initialization */ 15 ρ split ← ερ max // Split criterion /* Recursive decomposition */ 12 C ← Declare a global, empty set 13 C init ← Split database D // K-Means, SOM, BSOM 14 ρ max ← Maximal Hyper-Density of any clusters of C init 16 C ← split_clusters(C init ) 17 End.
Study design, population and data sources This is a modelling study comparing predicted hospitalization outcomes between people receiving HD-TIV and aTIV during an average influenza season. The study used hospitalization records of Australian adults ≥65 years of age from the National Hospital Morbidity Database from the Australian Institute of Health and Welfare [AIHW], Australian Government. In order to compute the average season hospitalization data (from 01 April to 30 November) were obtained from AIHW during 15 influenza seasons (2002-2017, excluding the 2009 H1N1 [human swine] influenza pandemic)  [35]. Admissions with a principal diagnosis according to the ICD-10 AM (International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Australian Modification) of respiratory (J00 to J99) and circulatory (I00-I99) diagnoses were included.
In this period the target group was vaccinated with SD-TIV, with a vaccination coverage rate of 75%  [37]. For each value of the VE of SD-TIV, we estimated the number of hospitalizations that would have occurred in that period if HD-TIV were used instead of SD-TIV, using the reported rVE of 17. 7% for cardiorespiratory  [29]  and 27. 4% for respiratory illness  [30]. Next, we estimated the number of hospitalizations that would have occurred in that period if aTIV was used, using the reported rVE for HD-TIV vs aTIV, 7% and 12% for cardiorespiratory and respiratory illness, respectively  [32]. Finally, we estimated the number of hospitalizations that would be averted by using HD-TIV instead of aTIV. Economic assessment of cost-benefit was based on the average unit cost per respiratory or circulatory event (Table  1 ), extracted from National Hospital Cost Database for the financial year 2019-2020.
Hospitalisation costs due to respiratory and circulatory causes have been estimated using the National Hospital Cost Data Collection Public Hospitals Report, Round 24. The cost per episode of hospitalisation was based on the average cost, weighted by number of separations, of the Australian Refined Diagnosis-Related Groups (AR-DRG) codes E62A and E62B for respiratory causes and F41AB/F42AB/F60AB/F62ABC/ F66AB/F72AB/B70ABCD for circulatory causes  [38]. As the vaccine costs were confidential, we explored the range of price increments that could be justified by the savings in the model. The 2018 population estimate was applied in the model to reflect a similar time period with hospital admission data in the study  [39]. The estimated national influenza vaccine coverage for older adults was also included in the model  [40, 41].
Discussion Enhanced vaccines for older adults are a solution to the reduced protection provided by standard dose vaccines in this population. The higher effectiveness of these vaccines may extend to circulatory disease prevention compared with standard-dose influenza vaccines  [42] , and differences between the available enhanced vaccines may also be important. This study showed that using HD-TIV instead of aTIV for Data for each calendar year were obtained from AIHW. The peak of influenza circulation in Australia is considered from April to November. Data for 2009 were excluded due to the H1N1pdm09 (human swine) influenza pandemic.
The QM region is limited to the ASR retinal chromophore; the MM interactions are modeled using the Amber forcefield  35  and the QM/MM ones with an electrostatic embedding approach, i. e. , including MM point charge electrostatic potential in the semi-empirical Hamiltonian, as implemented in COBRAMM. 28  The resulting data sets are labeled as pH3-AT, pH3-13C, pH5-AT, pH5-13C, pH7-AT, and pH7-13C. Notice that about 80% of the trajectories have been used for later analysis, the rest is discarded for reasons explained in SuppInfo. We analyzed each ensemble of trajectories using three different statistical properties. The first one, the isomerization quantum yield (IQY), characterizes the efficiency of the retinal photoisomerization. In principle, this quantity can be directly compared with the experiment, if the number of trajectories is large enough (i.
Interestingly, in the present simulations, D120 is 90% deprotonated at pH=3 in the AT conformation, Table  3 : Impact (in %, relative to the value in the protonated form, see SuppInfo) on thop , IQY and fitted time constant τ f ast of the deprotonation of several titrated amino acids, whose deprotonated fractions at the considered pH are indicated as x dep. In case of D98, the deprotonated dataset size at pH3 for the 13C isomer is too small to allow reliable fitting. while it is only 17% deprotonated in 13C. This allows us to analyze the deprotonation effect at the same pH value, revealing a conformation-independent 20% increase of the thop. Since this value cannot distinguish between the decay channels, we hypothesize that the D120 deprotonation results in a flatter S 1 potential energy surface at pH=3.
Our large dataset also offered the chance to analyze the impact of protonation state changes in individual amino acids. This revealed that some residues, like D57, affect the photochemical properties somewhat drastically. However, trends are sometimes difficult to extract, especially when the changes in the protonation state in one residue are correlated with other residues (i. e. , interacting amino acids). As a matter of fact, such clusters would need to be considered as a poly-acid featuring several effective pK a values that cannot be assigned to any particular amino acid. Two useful lenses to rationalize and investigate pH-dependent properties in complex systems are the "protonation microstate" and the "protein charge state". We showed that the former, while widely used to model proteins, may prove insufficient to accurately describe the problem in certain situations, mainly due to the small fraction of the total population it may represent.
Acknowledgement EP and NF thank the  French Agence Nationale de la Recherche  for funding (grant  ANR-14-CE35-0015-02 , project  FEMTO-ASR ). The  Centre de Calcul Intensif d'Aix-Marseille  is acknowledged for granting access to its high performance computing resources. Computational support and infrastructure was also provided by the " ´Centre for Information and Media Technology (ZIM) "´at the  Heinrich Heine University.
In addition, we have considered the cases with four BPSK subcarriers and eight BPSK subcarriers. The data are analyzed by reporting at two levels of the signal-to-noise ratio (SNR): SNR 10 dB and SNR 5 dB. The obtained results are shown in Table  1 , Figure  10. We notice that the proposed algorithm successfully detects a single subcarrier of the multi-carrier signal for the two SNR values of the additive white Gaussian noise. Thus, the noise presence in the analyzed signal still allows the detection of the OFDM signal.
Analyses were performed for BF and conducted in R using ASReml-R package version 4  (Butler et al. , 2017). The general model of the phenotype of an i th individual in the k th block at the l th trial for the j th season was y ljki = m + e lj + eb ljk + g i + ge lji + r ljki where m was the general mean across all trials, blocks within trials, seasons at trials, and individuals e lj was the fixed effect of the lj th environment (i. e. j th season at the l th trial) eb ljk was the fixed effect of the k th block at lj th environment g i was the average total (i. e. , additive + non-additive) genetic effect of the i th individual across environments with distribution N (0,Gg) where Gg was the variance-covariance matrix among average total genetic effects across environments given as Gg = I*v where I was the identity matrix of relationships among individuals and vg was the unknown total genetic variance across environments (where * was the Kronecker product) ge lji was the random environment specific total genetic effect of the i th individual at the lj th environment with distribution N(0,Gge) where Gge was the variance-covariance matrix among environment specific total genetic effects at each of the lj th environments given as Gge = I*Vge where I was the identity matrix of relationships among individuals, and Vge was the covariance matrix of environment specific total genetic effects among environments r ljki was the residual effect for each phenotype observation with distribution N(0,R) where R was a block diagonal matrix of residual covariance among seasons for individuals at each trial, i.
The total genetic covariance between environments lj and l'j' was cov GEI (lj,l'j')= v G +cov G×E (lj,l'j') , where cov G×E (lj,l'j') was the covariance from the variance-covariance matrix between environments lj and l'j'. Therefore, genetic correlation between environments lj and l'j' was estimated as cor(lj, l 0 j 0 ) = cov GEI (lj, l 0 j 0 ) ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi var GEI (lj) Â var GEI (l 0 j 0 ) p This correlation was used to estimate the magnitude of G×E due to ranking changes (when cor(lj,l'j') <1). Heatmaps of pair-wise genetic correlations were generated using the 'ggplot2' R package  (Wickham, 2016).
Heritabilities Broad-sense heritability was estimated in each location from the analysis of variance based on the following mixed model: y ijk = μ +g i + s j + b k + e where y ijk is the phenotypic value of the k th replicate of the i th individual in the j th season, μ is the mean value of the trait, g i is the random genotypic effect of individual i, s j is the fixed effect of season j, b k is the fixed effect of the block k (or replication), and e is the residual of the model. This linear mixed-effects model was fitted in R using the lme4 package  (Bates et al. , 2015). Broad-sense heritability of individual location clonal means (H 2 ) was then estimated as: H 2 = s 2 g s 2 g + s 2 e nr where s 2 g is the genetic variance, s 2 e the residual variance, n is the number of seasons and r is the number of replicates per genotype.
The opposite situation was found for the QTL on LG L1. The QTL on LG L1 explained in average 14. 8% of the variation in Murcia, and from 3. 1 to 7. 7% in the other four locations. A correlation coefficient equal to 0. 54 (p = 0. 013) was found between the PVE value of the QTL on LG L1 and the temperature in the 20 environments (Figure  S8 ). Finally, the QTL on LG L6 showed PVE values higher in Nimes (10. 2%) and Toulenne (12. 4%), compared to the other three locations (from 2. 9 to 5. 5%). For this QTL, no correlation with temperature data was found (correlation coefficient: -0. 056, p = 0. 82). For most QTLs, genetic and physical CIs were reduced with multi-location-multi-year analysis. For instance, QTLs on LGs R4 and L5 were detected within a CI of less than 0. 5 cM  respectively). The CI of the QTL on LG L1 (136.
Funding This work was supported by the " Reǵion Nouvelle-Aquitaine" in the project  " CerGEn " reference 20181R20203, number  00019006. INRAE BAP division and the "Reǵion Nouvelle-Aquitaine " cofunded the PhD scholarship of CB. This study received financial support from the  French government  in the framework of the  IdEX Bordeaux University "Investments  for the Future" program/GPR Bordeaux Plant Sciences.
Image Analysis Images were analyzed using ZEN 2. 1 (Zeiss, Jena, Germany), ImageJ software, and Arivis Vision 4D software (Arivis AG, Berlin, Germany). ZEN 2. 1 was used for postacquisition spectral unmixing, and analysis was performed on the resulting unmixed data. The intravital images were analyzed using the protocol described in  [7]. The presented images are pseudo-colored and contrast-enhanced for clarity. On fixed spinal cord sections, quantification was performed only on the EYFP channel of confocal Z-stack fluorescent images. An automated 3D segmentation pipeline was designed using the Arivis machine learning toolbox. Immune cells were counted in the total 3D volume of the confocal acquisitions using the Arivis software's machine learning object segmentation, which allowed the automatic detection of the two populations of CD11c + cells in a few minutes in the whole volume.
Patients cohorts Tissue samples from 91 consecutive patients, who had undergone nephrectomy in the Nice Hospital Urology Department, diagnosed with ccRCC (n=56), papRCC (n=22), chrRCC (n=6) or ONCO (n=7), were included. As defined by the 2022 WHO criteria, the diagnosis was based on pathology and cytogenetic analysis. H&E stained WSI (scanned using a Leica SCN400 Digital Slide Scanner, Leica Microsystems CMS GmbH, Wetzlar, Germany) used for diagnosis were collected to define a dataset consisting in a total of 201 WSIs.
RCC subtyping with Convolutional Neural Networks In Table  1  we report the accuracy on the test set at crop-level (mean ± standard deviation over the four classes of interest) obtained in our experiments concerning the canonical CNNs. The aim of this experimental validation is to identify a proper backbone for our ExpertDT, as well as to define a comparison term, as later detailed. As it can be gathered from the first column of Table  1 , irrespective of the depth and architectural complexity, none of the CNNs fully trained on the RCC dataset (column From scratch) was able to match the accuracy of the transfer learning frameworks (columns TL): the accuracy of the fully trained CNNs was almost 22% lower than the transfer learning based ones. These values suggest that the fully trained CNNs were not able to build a generalizable image representation on the given training set, most probably due to the high variability of the image characteristics, the intrinsic complexity of the RCC subtyping task  5  , and the relatively low number of patients used for training.
By taking into account more histologic subtypes, we have demonstrated the feasibility of classify approaching a daily use practice. This strategy also makes it possible to consider increasing the number of neoplasm entities under evaluation. Our ExpertDT can indeed be extended by placing the new entities in a strategic point of the decision-making tree worflow, according to the expertise of a skilled pathologist. Previously reported works are often based on exploitation of the TCGA database  3, 4, 15  , without rendering results at the WSI level  3, 4  or at the patient-level  3, 4, 15. In 2020, Fenstermaker et al. 3  proposed a CNN-based strategy to classify a selection of 5/10 3486 patches retrieved from WSIs, and belonging to three classes of interest: ccRCC, papRCC and chrRCC. The authors got a patch-level accuracy up to 99%, but they do not provide the WSI-level statistics.
The image database on which we carried out our study is developed from patients followed in our institution, and therefore presents a proportion of each of the entities linked to the epidemiology of these tumors. In addition, we have done our analysis by considering the WSIs with results on the patients level. These elements of our methodology are also closer to everyday practice. Some other recent works attempt to evaluate the impact of traditional machine learning pipeline, leveraging morphological hand-crafted features to discriminate between two  13, 14  or three  16  classes of RCC subtypes. These works are interesting to show the feasibility and improve the reliability of the classification, but these workflows and methodology do not consider the cumbersome differential diagnosis between chRCC and ONCO.
Up to our knowledge, just one previously published study by Zhu et al. 17  investigated the categorization in more than three classes of RCC neoplasm subtypes, including the difficult oncocytoma class, as we have done. This is partially due to the relatively low frequency of oncocytoma (3-7% of all renal neoplasms 2 ) and the consequent data scarcity. On the other hand, most of the previous research project exploited data gathered from TCGA data portal  18  , which is dedicated to malignant tumors, excluding renal oncocytoma cases. Even if the methodology and the results are close, there are notable differences between our work and those of the team of Zhu et al. 17. Their study consists in designing a classification process on a surgical resection database and then to test this methodology on TCGA and biopsy database.
Their workflow includes a data annotation step, with a manual ROIs definition on every WSI subsequently cropped via sliding window approach to be fed to the proposed ResNet18 classifier. In our experiment, we found normal CNNs (among which ResNet50) unable to reach good classification performance at patients level on our dataset. Thus, we proposed ExpertDT, which leverages pathologist's expertise and methodology to substantially improve the classification performance of state-of-the-art CNN models. It is interesting to note that not only our overall results are in the same range, from 77% to 100%, but also for our study and TCGA validation and biopsy validation for Zhu et al. study  17  , most of misclassification concerns ccRCC subgroup. We can assume that this is due to the fact that ccRCC, which is the most frequent tumor subtype, is also known as a tumor composed of clones and sub-clones, conferring intratumor and intertumor heterogeneity, in term of microscopic morphology but also grade, and genetic anomalies  [19] [20] [21].
As future directions, we intend to expand our dataset with other RCC cancers, including rare subtypes and classes as well as heterogeneous data. Furthermore, recent investigations suggest that self supervised learning methodologies may be a valuable solution to avoid the annotation step, the so-called ROI-cropping procedure  12, [22] [23] [24] [25]. As a matter of fact, this step, although useful to ensure supervised training efficiency and proper classification results, is time-consuming and incompatible with high speed use. In this regard, we intend to investigate the self supervised training of the binary CNNs classifiers, backbone of our ExpertDT.
State-of-the-art Convolutional Neural Networks We want to assess the efficiency of the state-of-the-art CNNs to correctly categorize the four different subtypes of RCC neoplasms and also to define the most performing training configuration. To answer these questions, three consolidated deep network models have been put into effect through TensorFlow framework: VGG16 26 , ResNet50  27  , and DenseNet121  28. CNNs typically need a large amount of labeled data to learn good visual representations, while preparing large-scale labeled datasets is expensive and time-consuming, especially for medical image data  9, 29. Hence, to avoid, or at least to limit, this tedious data collection and annotation phase, some researchers take as compromise ImageNet-pretrained convolutional neural network to extract visual representations from a large set of different image types, the last training steps being performed on a reduced medical images database  9, 29.
On top of this consideration, each CNN we implemented was trained following three different learning paradigms: (i) training from scratch; (ii) transfer learning leveraging ImageNet as source domain; (iii) transfer learning leveraging a different histological dataset as source domain. In this latter experimental configuration, we exploited the pre-training on the Colorectal Cancer (CRC) classification task described in a recent study by Ponzio et al. 9  to extract visual representations closer to our final target dataset, i. e. the RCC. To obtain a statistically significant dataset, in terms of inter-subjects and inter-class variability, specimen (i. e. WSIs) from 54 randomly selected patients were used as training set. Such WSIs were subsequently divided by a pathologist into regions of interest (ROIs) leveraging the so-called ROI-cropping procedure  25, 30  , consisting in: i) manually dividing each slide into ROIs that are homogeneous in terms of tissue content; ii) manually annotating the ROIs, imposing a unique label to each tissue category; iii) dividing the ROIs into a regular grid of tiles, that can be fed into the networks.