dataesr/scientific-paragraphs-categorization huggingface dataset:
Such an algorithm could be easily implemented in industrial applications with no previous recordings needed since the database can be entirely composed of synthetic data and still provide accurate classification results in different industrial sites regardless of their variety. Other advantages include the (3) electrical interpretability of the signatures and the (4) confidence index associated with the classification output. These characteristics could ease the troubleshooting process and the general interpretability of the algorithm. This is particularly interesting for industrial applications since understanding the algorithm decision-making process is essential for reliability issues. Future work will include the analysis of voltage sags' effects and consequences on industrial sites. The objective is to estimate the proportion of self-disconnected load types following a voltage sag using the electrical data of a single monitoring device installed at the main MV/LV transformer.
Class Database size Test set size Precision Recall F1-score NB-KDE RD A1 20 35 94. 87 100 97. 37 25. 58 86. 86 A2 20 157 100 98. 72 99. 36 66. 80 92. 84 B1 20 - - - - - - B2 20 90 100 100 100 87. 96 90. 01 C1 20 9 100 100 100 75. 24 80. 17 C2 20 - - - - - - D 20 94 100 100 100 8. 32 74. 02 Total 140 385 98. 97 99. 74 99. 34 52. 78 84. 78.
The OS of stimulated ewes and CTRL1 ewe is also characterized by the lack of Oari-SAL peptides in the 17 kDa band, although such peptides were retrieved in the 17 kDa band in the cycled CTRL2 and CTRL3 ewes. The presence of SAL in anoestrus ewes is not totally in accordance with our previous work  (18). Indeed, the ewes exposed to males in this work were previously included into the comparison of OS between SR and SA in a previous work  (18). In STIM2 and STIM3 ewes, Oari-SAL was not identified in 2D spots of sexual rest olfactory secretome, so we have suggested that Oari-SAL expression could be triggered by hormones in sexual activity period. In order to better understand this discrepancy, we amplified SALlike sequences from nasal tissue of CTRL1 ewe in season to ascertain the proteomics data and the presence of SAL sequences in the ewe secretome.
Two different sequences of Oari-SAL were expected from the Ensembl database of sheep genome, Oari-SAL1 (W5P8W4) and Oari-SAL2 (W5P8Y1), and their peptides were identified by mass spectrometry in sexual Days before male e ect protocol (T ) -d -d -d -d -d -d Progesterone (ng/mL) activity period  (18). Ten clones containing amplicons resulting from touchdown PCR revealed 6 different SAL-like sequences (Blastp) with mutations at 18 positions in the translated protein sequence (Supplementary Figure  2 ). Theoretical maps of trypsin digests of the six SAL sequences were obtained (https://web. expasy. org/peptide_mass/) to search for discriminant peptides in MALDI-TOF analyses of ewes OS. This diversity, although interesting, complexified the interpretation of presence/absence of SAL during the male effect.
Facing this multiplicity of sequences, a question raised about the accuracy of Oari-SAL absence in previous SR analyses  (18). Indeed, at the time of this previous work, we had not yet amplified the 6 different Oari-SAL sequences and did not include them in the query to the software search used with Nano-LC-MS/MS data (Mascot, Matrix Science, London). In the present work, Oari-SALs were identified with 10 peptides of different m/z, and for each analysis, at least one was part of the Oari-SAL1 and/or OariSAL2 of Ensembl database, meaning that even without the new sequences, SAL would be identified. New sequences were deposited in Genbank database under accession numbers MT921827 and MT921828. These data evidenced differences between cycled ewes (CTRL2 and CTRL3) and ewes in anoestrus at the beginning of the ram effect protocol (stimulated ewes and CTRL1 ewe), and confirmed the impact of hormonal status on the ewes olfactory secretome.
Analysis of the N-glycosylation of Oari-OBP(s) and -SAL(s) In contrast, N-glycosylation was described as one of the hallmarks of sexually active ewes OS  (18). Thus, the Nglycosylation profile of ewes OS included in this study were analyzed by reflectron positive ion mode MALDI-TOF mass spectrometry. After their release by enzymatic digestion, N- glycans were derivatized, allowing methylation of the hydroxyl and acetyl groups. The search for N-glycans was run on samples coming from 5 different ewes and this choice was determined by the quantity of samples remaining for each ewe. For the "true" control ewe CTRL1, and ewes STIM4 and STIM6, which have not responded to the male stimulation, no signal at all could be detected in their OS sample as expected (data not shown). For the analysis of stimulated ewes STIM1 and STIM3, the OS samples were pooled at each sample time to increase the sensibility (e.
Peptides were desalted by centrifugation in Pierce R C18 spin column (Fisher Scientific) according to manufacturer's instructions. Peptides were solubilised in 0. 1% trifluoroacetic acid and mixed with 10 mg/mL α-Cyano-4-hydroxycinnamic acid matrix (Sigma-Aldrich). Matrix Assisted Laser Desorption Ionization-Time Of Flight Mass spectrometry (MALDI-TOF MS) analysis was performed on a Voyager DE Pro mass spectrometer [ABI Voyager DE Pro MALDI-Mass Spectrometer (RRID:SCR_019340)]. The instrument was used in positive reflector mode, measuring peptide masses on a range of 500-4,000 Da. Spectra were analyzed with Data Explorer V4. 6 (Applied Biosystems). Raw data (. t2d files) were processed by using DataExplorer R software (ABSCIEX).
Results: The proposed method, kernel PCA Interpretable Gradient (KPCA-IG), provides a data-driven feature importance that is computationally fast and based solely on linear algebra calculations. It has been compared with existing methods on three benchmark datasets. The accuracy obtained using KPCA-IG selected features is equal to or greater than the other methods' average. Also, the computational complexity required demonstrates the high efficiency of the method. An exhaustive literature search has been conducted on the selected genes from a publicly available Hepatocellular carcinoma dataset to validate the retained features from a biological point of view. The results once again remark on the appropriateness of the computed ranking.
In this context, kernel methods offer a natural theoretical framework for the high dimensionality and heterogeneous nature of omics data, addressing their peculiar convoluted nature  [63]. These methods facilitate the analysis and the integration of various types of omics data, such as vectors, sequences, networks, phylogenetic trees, and images, through a relevant kernel function. Using kernels enables the representation of the datasets in terms of pairwise similarities between sample points, which is helpful for handling high-dimensional sample spaces more efficiently than using Euclidean distance alone. Euclidean distance can be inadequate in complex scenarios, as stated in  [17] , but kernels can help overcome this limitation. Moreover, kernel methods have the advantage of providing a nonlinear version of any linear algorithm which relies solely on dot products.
Kernel PCA Given a dataset of n observations x 1 ,. , x n with x i ∈ χ , a function k defined as k: χ × χ -→ R is a valid kernel if it is symmetric and positive semi-definite i. e. k(x i , x j ) = k(x j , x i ) and c T K c 0, ∀c ∈ R n , where K is the n × n kernel matrix containing all the data pair- wise similarities K = k(x i , x j ). The input set χ does not require any assumption. In this work we consider it to be χ= R p. Every kernel function is associated with an implicit function φ : χ -→ H which maps the input points into a generic feature space H , with possibly an infinite dimensional- ity, with the expression k(x i , x j ) = �φ(x i ), φ(x j )�. This relation allows to compute the dot products in the feature space, implicitly applying the kernel function to the input objects, without explicitly computing the mapping function φ.
Results We conducted experiments on three benchmark datasets from the biological domain to assess the accuracy of the proposed unsupervised approach for feature selection. These datasets include two microarray datasets, named Carcinom and Glioma, which are available in the Python package scikit-feature  [35]  and the gene expression data from normal and prostate tumour tissues  [10] , GPL93 from the GEO, a public functional genomics data repository. Glioma contains the expression of 4434 genes for 50 patients, while Carcinom 9182 genes for 174 individuals. Both datasets have already been used as a benchmark in numerous studies including several methods comparisons, such as  [7, 35]. Then, the dataset GPL93 contains the expression of 12626 genes for 165 patients, and it has been chosen for its complexity and higher dimensionality.
The idea is to compare the proposed methodology KPCA-IG with existing unsupervised feature selection methods from diverse frameworks, as conducted in  [7, 35] : • lapl  [23] , to include one method that relies on the computation of a score. • NDFS  [37] , to add one of the methods primarily designed for clustering. It is based on the implicit assumption that samples are structured into subgroups and demands the a priori definition of the number of clusters. • KPCA-permute in  [40]  available in the mixKernel R package to include another methodology from the context of kernel PCA. To evaluate the selected features provided by the four methods, we measured the overall accuracy (ACC) and normalized mutual information (NMI)  [15]  based on k-means cluster performance. For each method, the k-means clustering ACC and NMI have been obtained using several subsets with a different number d of selected features, with d ∈ {10, 20,.
, 290, 300}. Thus, the relevance of the selected values has been estimated according to their ability to reconstruct the clustered nature of the data. More specifically, the three datasets Glioma, Carcinom and GPL93 are characterized by 4, 11 and 4 groups respectively. Thus, the k-means clustering was computed using the correct number of clusters in the datasets to obtain a metric for the capability of the selected features to keep this nature. Note that only the NDFS method is implemented to explicitly obtain an optimum solution in terms of clustering, also requiring in advance the number of groups in the data. For each method, the k-means clustering was run 20 times to obtain a mean of the overall accuracy and normalized mutual information for each of the 30 subsets of selected features.
Both our novel method KPCA-IG and KPCA-permute have been employed with a Gaussian kernel with a sigma value depending on the dataset. The selected features are, in both cases, based on the first 3, 5 and 3 kernel PC axes for Glioma, Carcinom and GPL93, respectively. The CPU time in seconds required to obtain the feature ranking for all the methods has also been observed. The experiment was conducted on a standard laptop Intel Core i5 with 16GB RAM.
Evaluation on benchmarks datasets In Table  1 , we can see the results in terms of mean Accuracy and NMI over 20 runs for different numbers of retained features d. For the first dataset Glioma lapl seems to show the best performance in terms of NMI and AUC, except when d = 300 where the Accuracy obtained with KPCA-IG is the highest, even if all the methods seem to behave very similarly in terms of ACC. Analyzing the results for the other two datasets Carcinom and GPL93 that are considerably bigger and possibly more complex in terms of sample space manifold, the two methods based on the kernel framework exhibit to surpass the lapl and NDFS approaches, especially in the GPL93 datasets. The comparison of the different approaches in terms of NMI and ACC of these two datasets can also be observed in Figs.
1  and 2. Moreover, as shown in  [7]  NDFS and the other cluster-based methods like MCFS and UDFS suffer if the user selects an incorrect decision for the a priori number of clusters. In our case, we show that the proposed methodology behaves similarly or even better to a method like NDFS that is specifically optimized for this cluster setting. The two kernel-based approaches, namely KPCA-permute and our novel method KPCA-IG, reveal an excellent performance in this setting, once again displaying the appropriateness of the kernel framework in the context of complex biological datasets. However, KPCA-IG can provide these above-average performances with a considerably lower CPU time. Only lapl seems as fast as KPCA-IG while showing poorer results in the more complex scenario represented in this case by the GPL93 dataset.
Application on Hepatocellular carcinoma dataset Liver cancer is a global health challenge, and it is estimated that there will be over 1 million cases by 2025. Hepatocellular carcinoma (HCC) is the most common type of liver cancer, accounting for around 90% of cases  [39]. The most significant risk factors associated with HCC are, among others, chronic hepatitis B and C infections, nonalcoholic fatty disease, and chronic alcohol abuse  [44]. To analyse the use of the KPCA-IG method, we used the expression profiling by array of an HCC dataset from the Gene expression Omnibus (series GSE102079). It contains the gene expression microarray profiles of 3 groups of patients. First, 152 patients with HCC who were treated with hepatic resection between 2006 and 2011 at Tokyo Medical and Dental University Hospital.
Then, the gene expression of normal liver tissues of 14 patients as control  [12]. The third group contains the gene expression of 91 patients with liver cancer but of non-tumorous liver tissue. The total expression matrix for the 257 patients contains the expression of 54613 genes, and the data has been normalised by robust multichip analysis (RMA) as in  [19]  and scaled and centered before applying KPCA. To show the potentiality of KPCA-IG, we first perform kernel PCA with radial basis kernel with σ = 0. 00001 , which was set heuristically to maximize the explained variance and obtain a clear two dimension data representation. Even if detecting groups is not the optimization criterion of kernel PCA, it is possible to see that the algorithm catches the dataset's clustered structure in Figs.
7  and 8. For this reason, applying a method like the proposed KPCA-IG can enlighten the kernel component axes, possibly giving an interpretation of the genes' influence on the sample points representation. The KPCA-IG provides a feature ranking based on the KPCA solution, in this case, based on the first two kernel Principal Components. As mentioned before, one of the main advantages of the proposed method is the fast computational time required, as with this high-dimensional dataset, the CPU time was 654. 1 seconds. Table  2  presents the first 25 genes and Fig. 3  the distribution of the 54613 variables scores. To obtain an indication of the significance of the variables selected by KPCA-IG in terms of retained information, we computed the Silhouette Coefficient, a metric used to assess the goodness of a clustering solution.
The score values are in  [-1, 1]  where values close to 1 mean that the clusters are clearly separated  [6, 54]. In this case, even if KPCA is not optimized to create clusters of data, a 2D KPCA plot with clear separation among different groups may suggest a solution with more explained variability. Starting from the feature ranking given by KPCA-IG, we computed the silhouette scores for the KPCA solution for 5462 subsets in terms of original variables, i. e. datasets with the increasing number of features of 5, 15, 25,. , 54605, 54613. The metric was first computed using the features ranked by KPCA-IG and then with 5 different random feature rankings. From Fig. 4 , it can be seen that the scores obtained for kernel PC solutions applied to datasets composed by the features selected by KPCA-IG are consistently higher when compared to those where the ranking of the variables is chosen randomly.
To see more in detail the behaviour of the curves for reduced datasets with a small number of features, which can be more relevant for practical biological use, Fig. 5  represents the scores for 163 subsets of the form in terms of selected features of 5, 10,. , 100, 110,. , 1000, 2000,. , 54000, 54613. As we can see, the information retained by the KPCA-IG selected features is higher, as they lead to Silhouette scores for the KPCA plots closer to 1. All the obtained coefficients are based on a two cluster solution where the sigma parameter of the Gaussian kernel has been adapted to the different numbers of considered features to obtain a KPCA solution with maximized explained variance. Moreover, to check the generalization properties of KPCA-IG, we compared the explained variance captured by the selected variables on training and test data.
Conclusion We have seen how the unsupervised feature selection literature is narrower than its supervised counterpart. Moreover, algorithms that use the kernel principal component analysis for feature selection are reduced to a few works. In the present work, we have introduced a novel method to enhance variables' interpretability in kernel PCA. Using benchmark datasets, we have proven the comparability in terms of accuracy with already existing and recognized methods, where the efficiency of KPCA-IG has proven to be competitive. The application on the real-life Hepatocellular carcinoma dataset and the validation obtained from the comparison of the selected variables by the method with the bio-medical literature have confirmed the effectiveness and strengths of the proposed methodology. In future works, further in-depth analysis will be realized to assess the impact of the choice of the kernel function on the feature ranking obtained by KPCA-IG.
Availability of data and materials Glioma and Carcinoma datasets are freely available at https:// github. com/ jundo ngl/ scikit-featu re/ tree/ master/ skfea ture/ data, GPL93 is freely available at https:// www. ncbi. nlm. nih. gov/ geo/ query/ acc. cgi? acc= GPL93 HCC datatset is freely available at https:// www. ncbi. nlm. nih. gov/ geo/ query/ acc. cgi? acc= GSE10 2079.
By applying selective gates on the excited states reconstructed with MUGAST, it was possible to perform γ -particle spectroscopy and reconstruct the level scheme of the nucleus. The dataset collected using the self-supporting CD 2 target was employed not only for the spectroscopic study of the nucleus, but also to validate the kinematic reconstruction of the heavy partner and optimize the realistic parameters for the Monte Carlo simulation, such as the intrinsic resolution of the germanium detector, the smearing of the particle and γ rays and the measured angular distributions of the excited states  [53, 54]. Then a series of realistic simulations have been performed by varying the lifetime of the states of interest and the energy of the transitions in a sensible range. The normalised simulations were then compared with the experimental spectra using the least-χ 2 method to identify the minimum and extract the lifetime of the states, as shown in Fig.
In contrast, those for the ground state are consistent with large fluctuations about a spherical shape. This general picture is well reproduced by state-of-the-art model calculations, although it is important to note that some discrepancies remain. During the AGATA campaign at GANIL, Coulombexcitation data were collected as a by-product of experiments performed at near-barrier beam energies. For example, in a fusion-fission experiment using a 124 Xe beam on a 54 Fe target  [65] , "safe" Coulomb excitation of the beam on a nat W target contaminant was observed, and its analysis yielded spectroscopic quadrupole moments in 124 Xe  [66]. An ongoing analysis of slightly "unsafe" Coulomb-excitation data on 106 Cd, collected during an experiment aiming at lifetime measurements in 106,108 Sn  [67] , shows sensitivity to E3 strengths in this nucleus  [68].
Acknowledgements The authors are grateful to the AGATA, GRIT and VAMOS collaborations for having provided the material presented in this article and for the useful discussions. The authors acknowledge CERN, GANIL, LNL,  TRIUMF Laboratories  for having supported the experiments described in this paper. AG acknowledged the support of the  Italian Ministry of University and Research (MUR)  under the project CTADIR number " PRIN_2017P8KMFT ". WNC was supported by  STFC  under grant  ST/V001108/1.
In this review, we present eukaryotic diseases of flax and discuss possible management strategies. The causative pathogens, their lifestyle and affected flax organs are detailed in Table  2. There is no general survey or data on flax yield losses due to the different diseases, but the most damaging effects are due to wilts, rust and pasmo  [9]. Managing this broad range of diseases can be challenging because each pathogen has its own characteristics, such as its mode of transmission, the affected plant organ and the developmental stage of the plant when infection occurs. This review will focus on eukaryotic diseases that spread through the air, seeds and soil. Despite there being multiple modes of transmission, each disease is classified by its primary mode of spread. * Symptoms will be described and illustrated further in this review.
Flax displays a large genetic diversity reflected by several genetic collections. Among them, we find more than 11,000 accessions from 15 countries in the International Flax Database, more than 6000 accessions at the All-Russian Flax Research Institute and more than 5500 at the N. I. Vavilov Institute of Plant Industry stored in Russia; 3378 accessions from 76 countries are maintained at the Plant Genetic Resources of Canada. A reduced core collection is available that captures the diversity spectrum in the whole collection  [6, 48, 119, 120]. In flax, several qualitative and quantitative genetic resistances are systematically included in breeding programs, e. g. , genetic resistance to rust in Canadian linseed cultivars and to scorch or powdery mildew and Fusarium wilt in European fiber flax.
Acknowledged Mode of Action- Target Site Code Chemical Group- FRAC Code Active Substance Resistance Risk Target Disease * Areas of Authorization A Contact fungicide action Chemicals with multi-site activity Inorganic-M02 Sulfur Low Powdery mildew BE Data not available Inorganic Potassium hydrogen carbonate Data not available Powdery mildew NL Signal transduction-E2 Phenylpyrroles-12 Fludioxonil a Low to medium Gray mold NL Unknown Phenyl-acetamides-U06 Cyflufenamid Unknown Powdery mildew BE, FR Systemic fungicide action Cytoskeleton and motor protein-B1 MBC-Thiophanate-1 Thiophanate-methyl High Anthracnose Pasmo Fusarium wilt BY SDHI-pyrazole-4-carboxamides-7 Benzovindiflupyr b Bixafen Fluxapyroxad c Medium to high Pasmo Powdery mildew Sclerotinia stem rot GB, IE NL, CA Respiration-C2 SDHI-pyridine-carboxamides-7 Boscalid Medium to high Basal stem blight Foot rot Powdery mildew FR NL SDHI-pyridinyl-ethyl-benzamides-7 Fluopyram d Medium to high Gray mold Sclerotinia stem rot NL Anthracnose BY Browning and stem break BY, FR QoI-methoxy-acrylates-11 Azoxystrobin e High Fusarium wilt Pasmo BY BY, CA, FR Powdery mildew CA, FR Sclerotinia stem rot CA, FR, NL Respiration-C3 Picoxystrobin High Pasmo CA Anthracnose BY Fusarium wilt BY QoI-methoxy-carbamates-11 Pyraclostrobin c High Pasmo BY, CA Powdery mildew NL Sclerotinia stem rot CA QoI-oximino-acetates-11 Trifloxystrobin High Alternaria blight Powdery mildew NL Amino acid and protein synthesis-D1 AP-anilino-pyrimidines-9 Cyprodinil a Medium Gray mold NL Anthracnose Cyproconazole Medium Fusarium wilt BY Pasmo Anthracnose BY Basal stem blight FR Browning and stem break FR Sterol biosynthesis in membranes-G1 DMI-Triazoles-3 Difenoconazole e Medium Foot rot FR Fusarium wilt BY Pasmo BY, CA, FR Powdery mildew BE, CA, FR, NL Sclerotinia stem rot CA, FR Anthracnose Epoxiconazole Medium Fusarium wilt BY Pasmo.