dataesr/scientific-paragraphs-categorization huggingface dataset:
Literature review The literature search returned 2,147 entries in PubMed and 3,983 in EMBASE. The nonduplicate articles were 4,333, of which 3,746 were discarded based on the title and abstract (Figure  1 ). Of the remaining 587 articles, 547 were discarded for not matching the inclusion criteria (of these, 62 were not further considered since no reported influenza cases were type B), 7 for being based on the WHO FluNet and/or GISAID database, 3 for being previous reports from the GIHSN, and 4 because of overlap with more recent and/or larger articles based on the same data sources. A total of 26 articles were included in the systematic review: their main characteristics are reported in Table  1   In Table  1 , studies were ordered according to the start and the end of the observation period.
WHO-FluNet, GIHSN, and GISAID On August 28 th , 2023, we downloaded data from the WHO-FluNet for the period from week 1 of 2020 to week 33 of 2023. The database contained around 31. 93 million respiratory specimens and 1,998,172 million influenza detections, of which 379,699 (19. 0%) were influenza B viruses. Of these, 272,236 (71. 7%) were not characterized, 107,048 (28. 2%) were B/Victoria lineage, and 415 (0. 1%) were B/Yamagata lineage. The average number of influenza B/Yamagata detections per 100,000 processed specimens dropped from 14. 9 in the weeks 1-17 of 2020 to below 0. 10 in 2022 and 2023 (Table  2 ). Influenza B/Yamagata accounted for a significantly greater proportion of detections during the weeks 18-52 of 2020 compared to the earlier weeks in the same year. This was followed by a major drop in the frequency of influenza B/Yamagata detections in 2021 and 2022 (Table  2 ): the number of influenza B/Yamagata detections dropped from 364 in 2020 to 45 in 2021 and three in 2022 (two from Germany and one from North Korea).
In the first 33 weeks of 2023, there were 6 B/Yamagata detections globally, of which five were from Cameroon and one from Cuba: this represented a slight increase compared to 2022 but remained well below the numbers in 2020 and 2021 (Table  2 ). In summary, the rate of B/Yamagata detections entered in the WHO-FluNet database dropped significantly worldwide after 2020, both in absolute terms and as a percentage of the total number of influenza and influenza type B detections (Table  2 ). The number of influenza detections within the GIHSN is reported in Table  3. Out of a total of 8,590 influenza detections reported between January 2020 and week 27 of 2023, 2,055 (23. 9%) were influenza B. Of the 978 influenza B viruses that were characterized, seven (0. 7%) were B/Yamagata, all detected in 2020 (two in France, two in Mexico, one in India and one in Russia in the first three months of the year, and again one in India in week 51).
No B/Yamagata influenza detections were reported from 2021 onwards. On September 9 th , the GISAID database was queried for all influenza B/Yamagata and however, no sequences of other segments were available for definitive assessment. In conclusion, no sequences of wildtype viruses of the B/Yamagata lineage with a collection date after March 2020 have been uploaded to GISAID and GenBank, including the viruses reported as B/Yamagata lineage to FluNet and GIHSN after March 2020 (of note, therefore, for these later viruses there was no objective assessment possible whether these viruses were wildtype or vaccine-derived).
g. two H3N2 strains) would be worth discussing. Of note, there are other issues that are important beyond those concerning vaccine formulation and development. In particular, if B/Yamagata viruses are considered extinct, the question arises as to whether influenza laboratories around the world should change the security level with which they are allowed to handle B/Yamagata viruses, due to the potential risks associated with the escape of an extinct influenza virus. A few aspects relating to the sources of information that we researched deserve comment. The WHO-FluNet database is dynamic in that the data it contains can change over time. For example, a NIC or reference laboratory in a given country may need to make corrections to data entered in previous weeks. As far as our current research question is concerned, it is worth noting that the WHO-FluNet database does not presently have an option to report vaccine-derived detections separately.
We are aware that WHO proactively investigates all reports of B/Yamagata entered into their global surveillance databases and these are sometimes removed at a later stage when laboratory analyses reveal that they are vaccine-derived. In preparing this article, the 2020-2023 data from the WHO-FluNet database were downloaded twice, on May 19 th and then again on August 28 th 2023. In this short time period, the number of reported B/Yamagata influenza cases decreased from 45 to 42 for the year 2021, and from 8 to 3 for the year 2022. Hence, although the number of B/Yamagata detections in 2023, as shown in Table  2 , slightly exceeded the number in 2022 (6 vs. 3), this might be an overestimate compared to reality (and the same considerations may apply to the GISAID database). A further limitation is the underrepresentation of tropical countries, particularly in populous South-East Asia and Africa, in data gathered from the review of scientific literature and webpages of respiratory viruses' surveillance systems.
The B/unknown-lineage dataset included 128 unique viruses. The blast and phylogenetic analysis revealed that the majority (n=111) of these were wildtype B/Victoria and 7 were derived from the study published in 2023 by Aslam et al. on historic B viruses for influenza vaccine virus construction [Aslam 2023]. Of the remaining 10 viruses, two collected in the USA in 2021 and one from South Korea with data collection in 2020 were highly likely LAIV derived. Finally, seven waste water specimens collected in March 2020 in New York had partial PB1 sequences similar to 2007/2008 B/Yamagata lineage viruses,.
Morphological Analysis Liver samples were dehydrated in a graded ethanol series and embedded in paraffin wax. Paraffin blocks were made and cut (4 µm thick) in a Leica 2055-Autocut microtome (Leica Instruments GmbH, Nussloch, Germany) and stained with hematoxylin and eosin  [27]. Micrographs from each slide were taken using a Nikon Microphot-FXA microscope (Nikon Instruments Inc. , Melville, NY, USA) incorporated with an Olympus DP50 camera (Olympus Optical Co. , LTD, Shinjuku-ku, Tokyo, Japan). The total area of 50 hepatocytes per specimen (450 hepatocytes per experimental diet) was measured as well as the maximum and minimum hepatocyte length, considered the hepatocyte nucleus as a reference point. All the measurements were carried out with ImageJ software (version 1. 53e) using arbitrary units.
Statistical Analysis Data are presented as mean ± standard deviation (SD) or as mean ± standard error of the mean (SEM). Individual fish was the experimental unit for morphometric data and data on gene expression (n = 27 with 3 or 9 fish originating from each of the 3 replicate tanks). Tanks (n = 3) were used as the experimental unit for data on lipid and fatty acid composition due to the small size of samples, so a pool of 3 fish originating from each of the 3 replicate tanks was used. Differences were considered significant when values of p < 0. 05. Comparisons between two treatment groups (CTRL and S-ASTA) were assessed by the t-Student test. All statistical analyses were performed using SPSS (IBM, Chicago, IL, USA).
Acknowledgments: The authors wish to thank P. Borges for his valuable input in the initial project and F. Terrier, A. Lanuque and F. Sandres for the preparation of diets and care of fish. They are also very grateful to A. Surget, M. Cluzeaud, L. Attard, and L. Bouakaz for technical assistance and C. Heraud, K. Dias and M. Parailloux for sampling.
Funding: The project leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 652831 (AQUAEXCEL 2020 ). Institutional Review Board Statement: All the protocols involving animals in this experiment were strictly performed in compliance with the European Directive 010/63/EU for the protection of animals used for scientific purposes, and the French Decree no. 2013-118 for animal experimentation. Informed Consent Statement: Not applicable.
The term "labeled data" refers to the process of matching specific equivalent circuits with their corresponding EIS spectra. This matched set is then used to train a supervised ML algorithm, enabling it to accurately determine the equivalent circuits for EIS spectra that are not yet labeled. Though manual labeling is feasible, the sheer volume of datasets makes this task both time-intensive and susceptible to human error or subjectivity. An alternative approach highlights the use of synthetic EIS data, where the dataset is generated numerically from predefined equivalent circuits. [13, 22]  While this method appears to circumvent the previously mentioned challenge, it still necessitates prior knowledge regarding the specific equivalent circuits to be modeled. In this study, we endeavor to surmount the mentioned challenges associated with the need for labeled data by employing unsupervised ML methodologies.
These include guiding the discovery of electrochemical reactions in molecular electrochemistry,  [29]  elucidating structure-property relationships in concentrated electrolytes,  [30]  clustering particle reactivity in Al alloys,  [25]  and pinpointing patterns in voltammograms,  [31]  among other achievements. [32- 34]  In this context, the primary objective of this study is to harness the latest strides in unsupervised ML to streamline the processing of extensive EIS datasets. In our exploration of unsupervised methodologies, we prioritized dimensionality reduction algorithms due to their effectiveness in simplifying complex data. [35, 36]  We employed both linear techniques, such as principal component analysis (PCA),  [37]  known for its interpretability and straightforward hyperparameter tuning, and non-linear methods, like t-distributed stochastic neighbor embedding (t-SNE)  [38]  and uniform manifold approximation and projection (UMAP).
[39]  In the context of dimensionality reduction, linear methods assume a straight-line relationship between input variables, while "non-linear" methods assume more complex, curved relationships in the data (vide infra). Despite t-SNE and UMAP being more sensitive to hyperparameter settings, they were chosen for their ability to reveal underlying data structures that linear methods might miss. Our main research goal was to find out if unsupervised methods are capable of accurately identifying spectra that correspond to different equivalent circuits based on noise-free synthetic dataset. For situations where these methods fall short, we were committed to investigating ways to overcome these limitations. Furthermore, we examined the practical applications of these unsupervised methods for electrochemists who wish to implement them in their research.
Data Generation We based our research on the synthetic database that consisted of more than 9,000 spectra related to 18 typical equivalent circuits (8 examples of them is shown in Fig. 1 , the rest can be found in SI), pertinent to areas such as electroanalysis,  [40, 41]  electrocatalysis,  [42, 43]  corrosion,  [44, 45]  batteries,  [10, 11]  and sensors. [9, 46]  It is worth noting that we deliberately omitted equivalent circuits yielding identical EIS spectra, as their differentiation is inherently unfeasible. The values for resistance (R), capacitance (C), constant phase element (CPE), inductance (L), and Warburg element assuming semi-infinite linear diffusion (W) were selected based on typical specifications found in pertinent literature  [9] [10] [11] [40] [41] [42] [43] [44] [45] [46]  and tabulated in SI.
Subsequently, EIS spectra were calculated based on chosen equivalent circuits encompassing all feasible combinations of these predetermined values. From this pool, we randomly selected 500 spectra (large enough to illustrate statistical significance) representing each equivalent circuit, putting them into a singular dataset. Before the dimensionality reduction process, all impedance values were normalized to keep the impedance modulus within the 0 to 1 range, ensuring only the relative shape of impedance curves, not the absolute magnitude, influenced the dimensionality reduction process. The original Python code, encompassing both the generation of all EIS spectra and the data processing elucidated in this manuscript, is available in the Zenodo repository. Note that we intentionally omitted the addition of artificial noise or parasitic capacitances to our synthetic data.
Additionally, the spectra span a wide frequency range from 10 5 to 10 -2 Hz. These factors contribute to their distinct artifact-free shapes, which are easily recognizable, as demonstrated in Nyquist plots (refer to Fig. 1 ). This method was chosen to determine if dimensionality reduction algorithms could match the accuracy of human analysis with clear, noise-free data, that in future, can be used as a foundation to address the complexities introduced by experimental artifacts. It is crucial to emphasize that our study's relevance is unaffected, as removing noise and minimizing parasitic effects are common preliminary steps in experimental data analysis,  [46] [47] [48] [49] [50] [51]  resulting in clean, distinct shapes akin to those in our dataset, which already reflects the important body of practical scenarios.
Practical Aspects of Dimensionality Reduction in EIS From a practical standpoint, dimensionality reduction techniques are highly beneficial in the initial stages of processing large impedance spectroscopy datasets, uncovering inherent numerical patterns. To illustrate this, consider processing an experimental dataset of 20,000 impedance spectra. Employing the UMAP method, as demonstrated in our study, may reveal, for example, 8 distinct clusters in the data (as illustrated in Fig. 1 ). This would suggest the necessity for at least 8 different equivalent circuits to adequately model all spectra in the dataset. A practical approach to identify these circuits involves selecting and manually analyzing a few spectra from each cluster, greatly reducing the analytical burden compared to manual examination of the entire 20,000-spectrum dataset.
It is important to note that dimensionality reduction may not distinguish spectra with interchangeable C and CPE elements. To overcome this limitation, one could apply supervised ML. This involves generating a synthetic dataset of EIS spectra using the identified equivalent circuits, both in their original form and with C replaced by CPE elements. Training a supervised ML model on this dataset allows for 99 % accurate classification of spectra into their corresponding circuits, as supported by literature. [11, 13, 14]  However, it is crucial to recognize that this approach cannot be directly applied to the original dataset of 20,000 spectra, as the necessary equivalent circuits for model training are initially unknown. For practical implementation, we provide our neural network architecture in the SI, along with guidance for adaptation to specific datasets.
Conclusion Our exploration into the domain of unsupervised clustering for EIS data interpretation reveals both potential and pitfalls. We showed that non-linear dimensionality reduction methods, notably t-SNE and UMAP, proficiently cluster varied impedance spectra but contend with the difficulties of closely aligned circuits, especially in scenarios substituting a C with a CPE. This issue seems to be intrinsically rooted, as spectra from both C-and CPE-type circuits exhibit similar numerical patterns when evaluated solely on numerical data. This finding highlights a possible fundamental limitation and emphasizes that unsupervised ML methods alone might not completely capture the complexities of electrochemical data. In the manuscript's conclusion, we present a technical note detailing how our discoveries in unsupervised clustering, when integrated with the proven effectiveness of supervised ML, can facilitate the automated processing of extensive impedance datasets.
SI1 -Creation of dataset The creation of the EIS dataset started with the definition of equivalent circuits. This circuit was constructed using a combination of resistive (R), capacitive (C), constant phase element (CPE), inductive (L) components, and Warburg element (Wo). The created circuits are: 1. R0-C0, 2. R0-CPE0, 3. R0-p(R1,C1), 4. R0-p(R1,CPE1), 5. R0-p(R1-p(R2,C2),C1), 6. R0-p(R1-p(R2,C2),CPE1), 7. R0-p(R1-p(R2,CPE2),C1), 8. R0-p(R1-p(R2,CPE2),CPE1), 9. R0-p(R1-Wo1,C1), 10. R0-p(R1-Wo1,CPE1), 11. R0-p(R1,C1)-p(R2,C2)-p(R3,C3),\ 12. R0-p(R1,CPE1)-p(R2,CPE2)-p(R3,CPE3), 13. R0-p(R1,C1)-p(R2,C2)-Wo1, 14. R0-p(R1,CPE1)-p(R2,C2)-Wo1, 15. R0-p(R1-p(L1,R2),C1), 16. R0-p(R1-p(L1,R2),CPE1), 17. R0-p(C1,R1-L1,R2-p(R3,C3)), 18. R0-p(CPE1,R1-L1,R2-p(R3,C3)) After defining the circuit, we initialized the parameters for each element within it.
This encompassed setting the values for resistance, capacitance, CPE parameters, inductance, and Warburg elements. Elements were mapped to the corresponding values of parameters. To create a diverse dataset, we generated random combinations of the parameters. These combinations allowed us to simulate various realizations of the equivalent circuit. For each set of parameters, the impedance of the circuit at the specified frequencies was calculated using an Impedance Python library. 1  The starting and ending points of the frequency range were set using a logarithmic scale start frequency 10 5 Hz and end frequency 10 -2 Hz Values for elements of equivalent circuits were varied in wide ranges, for example, for the R0-p(R1-p(R2, CPE2), CPE1) a circuit the values are shown in table s1:.
It enables the identification of characteristic features in the impedance data, such as peaks, troughs, and inflection points, which are important for understanding the electrochemical behavior of the system. In the realm of unsupervised dimensionality reduction, standardization of data is commonly employed, where features are rescaled to have zero mean and a standard deviation of one, facilitating a uniform scale across different dimensions. However, in our analysis, we observed a notable deviation from this norm. Algorithms applied to our dataset exhibited enhanced performance when the data underwent normalization, specifically scaling each feature to a range of 0-1. This approach, divergent from the traditional standardization method, suggests that normalization might be more effective in context of impedance spectroscopy, potentially due to its capacity to maintain the original proportion of data values within a bounded scale.
The methodology involved the following steps: Utilization of dimensionality reduced data and pre-assigned Labels: The dimensionality reduction algorithms were used to process the high-dimensional impedance data to 2D projections. Each point in this reduced space represented an EIS curve and was associated with a label indicating its expected cluster, based on the equivalent circuit it represented. Silhouette Score Computation: The silhouette scores were calculated directly from these 2D projections, using the pre-defined labels as a reference for cluster membership. This calculation entailed: Determining the average intra-cluster distance (denoted as a) for each point, which is the mean distance to other points within the same cluster. Identifying the average nearest-cluster distance (denoted as b) for each point, defined as the mean distance to points in the closest cluster that the point does not belong to.
These limitations become more pronounced in the context of convex and non-convex clusters: Assumption of Convex Clusters: The silhouette score generally assumes that clusters are convex and isotropic, meaning they are roughly spherical and similar in size. This assumption works well for clustering algorithms like k-means, which are designed to identify convex clusters. However, in many real-world datasets, including those involving EIS curves, clusters may not adhere to this shape or size. Non-convex clusters, which are common in complex data sets, may not be accurately represented by their silhouette scores. Difficulty with Non-Convex Clusters: In the presence of non-convex clusters, the silhouette score might give misleading results. For example, it might fail to recognize that two closely intertwined but distinct clusters are separate entities.
This is because the silhouette score relies heavily on the concept of intercluster and intra-cluster distances, which may not accurately capture the relationships in non-convex clusters. Sensitivity to Noise and Outliers: The silhouette score can be sensitive to noise and outliers in the data. Outliers can skew the average distances used in the silhouette calculation, leading to potentially misleading scores. In datasets where noise and outliers are present, the silhouette score may not reliably reflect the true clustering quality. Dependence on Distance Metrics: The effectiveness of the silhouette score also depends on the choice of distance metric used in the clustering algorithm. Different metrics can yield significantly different clustering results, and consequently, different silhouette scores.
This dependence can be particularly challenging in datasets with complex structures, like EIS curves, where the choice of an appropriate distance metric is not straightforward. Limitation in Revealing Cluster Structure: While the silhouette score can indicate how well-separated the clusters are, it doesn't provide information on the actual structure or distribution within the clusters. In cases where understanding the internal structure of each cluster is crucial, additional metrics or visualizations would be necessary. Based on this, silhouette score can be misleading. Visual analysis of 2D projections is needed.
Each principal component is a linear combination of the original variables. PCA reduced dimensionality by focusing on the principal components with the highest variances. The lower-variance components are often considered to be "noise," and by ignoring them, PCA can filter out noise and leave the more meaningful structure. This is particularly useful when the data has many variables and the relationships between them are not immediately obvious. PCA is widely used in exploratory data analysis because it reveals the internal structure of the data in a way that best explains the variance in the data. If a dataset is visualized as a set of coordinates in a highdimensional data space, PCA can supply the user with a lower-dimensional picture, a "shadow" of this object when viewed from its most informative viewpoint.
Consequently, each curve can be thought of as a point in a 320-dimensional space (160 points, each with a real and imaginary component). Dimensionality Reduction with PCA: Given the high-dimensional nature of these curves, PCA is utilized to reduce the dimensionality to a more manageable two-dimensional space. This reduction is achieved while retaining the key characteristics and variances of the original dataset. The process involves: Normalization: First, the impedance data for each curve is normalized to ensure each dimension contributes equally to the analysis. Covariance Matrix and Eigenvectors: The covariance matrix of this standardized data is computed, followed by the calculation of its eigenvalues and eigenvectors. Projection onto Principal Components: The dataset is then projected onto the first two principal components.