dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS We thank the  Tara Oceans consortium , the  EukBank consortium , and the people and sponsors who supported the Tara Oceans Expedition (http://www. embl. de/taraoceans/) for making the data accessible. This is contribution number 146 of the Tara Oceans Expedition 2009-2013. Computational time was provided by the  Supercomputer System, Institute for Chemical Research, Kyoto University. This work was supported by  JSPS/KAKENHI  (Nos. 18H02279  and  19H05667  to HO), the  Collaborative Research Program   of the Institute for Chemical Research, Kyoto University  ( 2020-29  to KT), and  JST SPRING , Grant Number  JPMJSP2110  (to HK),  France Génomique  ( ANR-10-INBS-09  to PW), a  CNES  postdoc fellowship  2019-2021  to REH,  ERC Advanced Award Diatomic  (Grant agreement No. 835067  to CB), and the  Horizon Europe  project ' Marco-Bolo ' (Grant Agreement No.
DATA AVAILABILITY Figures S1-S19, Tables  S1  and S2 , Data S1-S3, and S1 are provided as supplementary materials. Video S1 shows the 19-year time series of community-type distributions predicted from satellite-derived parameters, related to Fig. 6. Newly sequenced Tara Oceans 18 S V4 data have been deposited to EMBL/EBI-ENA: PRJEB6610 (Tara Oceans), PRJEB9737 (TARA Oceans Polar Circle). Data and codes used in the analysis are available at the GenomeNet FTP: https://www. genome. jp/ftp/ db/community/tara/Satellite/. Essential codes are also available at the GitHub repository: https://github. com/hirotokaneko/plankton-from-satellite.
Moreover, in  [20] , five predefined pulse waveforms corresponding to certain pathological conditions were used to train the convolutional neural networks (CNN) model, while, in our case, the data were derived from the available small data. This paper introduces the joint acquisition options of pulse waves from five selected arteries by using the techniques of EBI and PPG. Moreover, four different excitation frequencies were considered in concurrent EBI signal acquisition. Together with EBI, rPPG was picked up, providing means for direct comparison and consideration to be applied in sensor-fusion-based applications. A qualitative and quantitative evaluation of the signals was performed to assess the suitability of the selected locations for the wearable monitoring of peripheral artery EBI and PPG.
In summary, VAE is an architecture that relies on a probabilistic description for observing the latent space formed through the encoding and decoding of the data  [42]. The goal of VAE is to generate similar data from random features that are gained from the small data by compressing the high-dimension input to its lower-dimension representations  [10]. However, none of the described methods propose a 100% working solution for small data deep learning. Pre-knowledge of the data or an existing database containing all possible samples is still needed for the best outcome. However, for small data, tailor-made solutions based on the above-described deep learning methods are highly suitable.
The model was composed in Python 3. 11 by using sklearn, and 5-fold cross-validation was applied to set the weights of training/testing to the ratio of 4:1. The general workflow of the proposed method is visible in Figure  2. The proposed deep-learning-based model was then applied to all of the defined periods of the signal. For each case, a confusion matrix was generated to estimate the accuracy and probability. Since we have a classification problem (not binary classification) and the data were fully balanced, we used a simple approach-illustrated by Equation (3). The accuracy (Acc) was calculated according to (denoting the percentage of classifying the signal correctly): Acc = Pred correct /Pred total (3) where Pred correct denotes the number of correct predictions and Pred total denotes the total number of predictions, i.
The basic idea of CNN is that the data in a model are transferred from layer to layer, whereas the trivial features are extracted in the first layers and complex features in the deeper layers. In this study, the model was developed using the Python programming language and the TensorFlow framework with Keras as its high-level application programming interface (API), running on the cloud-based development environment provided by Google Colab. The underlying operating system used was determined to be Linux. The simulations were run on a standard machine without using graphics processing units (GPUs) or tensor processing units (TPUs). The architecture with the parameters of the proposed CNN is visible in Table  1  and infrastructure in Figure  3. The proposed model has nine layers (pooling layers can be omitted), of which six have trainable parameters.
Results Three consecutive measurements were performed at each chosen sensor location, resulting in three data logs of EBI and rPPG in the time scale. The analog signal was sampled by quite a high frequency-1666 Hz-to possess the opportunity of restoring the signal as rigorously as possible. While the rPPG signal was relatively clean and free from interference, the EBI signal required filtering. For the detection of ∆Z(t) and ∆rPPG(t), the signals were post-processed in LabView software by using a Savitzky-Golay filter at side points of 100. The sample signal waveforms of EBI and rPPG, acquired from all five chosen locations on the skin surface at an excitation frequency of 100 kHz are visible in Figure  4a-e. For quantitative evaluation, the ∆Z(t) and ∆rPPG(t) were calculated for all three acquired signals of EBI and rPPG from each chosen position, constituting an essential quality measure.
Limitations of the Work The presented research is limited in certain aspects that need attention in considering the gained results. The signals were acquired only by involving a single volunteer. As a result, the lack of inter-individual variability appears, requiring a larger sample size for possible extended research. However, performed as a pilot study, the results are acceptable and considerable for publishing. The presented and applied deep-learning-based model needs verification on a larger amount of data to verify its accuracy and probability. Moreover, the model needs testing on different formats of biomedical signals; for example, in the context of EBI (dZ/dt), its first derivative-commonly known as the ICG. As in the case of dZ/dt, the peaks in the signal of ICG become narrower-the same appears when differentiating the signal of rPPG-and the signal morphology will change, possibly affecting the performance of the deep learning model.
3) Population The study involved two distinct populations. The study population for the main objective was composed of the unit's caregivers, including nurses, medical resident and doctors on the ward. This population consisted of volunteer caregivers who agreed to answer the questionnaires anonymously. We excluded nurses who were unavailable during the time available for the study because of night shifts, safety rest, holidays etc. The course of the intervention is presented in the Fig. 1. The study population for the second objectives was composed of a cohort of consecutive patients aged 75 years old and older, hospitalized in the Infectious Diseases Department of the CHMS of Chambéry, and had not objected to the extraction of data from their medical le. Patients under guardianship or subjects deprived of their liberty were excluded.
7) Statistical Analyses We performed descriptive analyses with qualitative data described in numbers and percentages. Quantitative variables were described as mean +/-standard deviation or median with 25th and 75th percentile; qualitative variables were expressed in number and percentages. The 2 sets of data were considered with a one-sided with paired data (same subjects interviewed and same questions asked before and after the intervention). The p-value calculations were carried out using a Student's T-test. Analysis was performed using data processed in Excel 2019 for PC. The signi cative level retained was the p-value below 0. 05. Given the absence of a control group in this quasi-experimental study and the possible seasonality in the occurrence of our major secondary endpoint, we compared our data to the confounding prevalence of patients above 75 years of age recorded in the unit over the previous three years.
8, 9 A variety of factors are thought to have contributed to these unusual patterns of influenza activity. [12] [13] [14]  The "Global Influenza Surveillance and Response System" (GISRS), managed by the World Health Organization (WHO), is the primary global mechanism and resource for the surveillance and control of influenza. 15, 16 This system was established in 1952, celebrating its 70th birthday in 2022, and currently encompasses 146 National Influenza Centers (NICs) based in 123 countries. 17, 18 NICs play a critical role in pandemic influenza risk assessment and the WHO recommendations for the influenza vaccine composition by conducting and sharing surveillance data to the FluNet platform. 16, 19, 20 FluNet is a dataset that provides publicly available, national level, weekly influenza surveillance data.
All NICs adhere to common protocols/ regulations and are periodically evaluated. Yet, major differences in the characteristics of their surveillance system exist (e. g. , geographical representativeness or case definitions). These differences can impact the quality and representativeness of data reported to FluNet. 21  addition, the reliability, completeness, and accuracy of the influenza surveillance data are largely driven by national healthcare infrastructures and appear positively associated with a higher number of NICs as well as participating (sentinel) sites and greater health expenditures. 22 nce the beginning of the pandemic, these national influenza surveillance systems have been leveraged for the surveillance of the SARS-CoV-2 virus. 20 As a result, many NICs became COVID-19 testing centers that functioned alongside national public, and private sector COVID-19-specific testing centers.
23 To monitor the impact of the SARS-CoV-2 pandemic on influenza activity and surveillance, the Flu-CoV project (nivel. nl/en/flucov) was initiated in 2021. As part of this project, we published a monthly epidemiological bulletin portraying SARS-CoV-2 activity alongside influenza in 22 countries around the world. In addition, the project consisted of a survey to be circulated digitally among NICs located in the countries portrayed in the bulletin. The survey aimed to increase our understanding of national influenza surveillance structures and how these were affected by the COVID-19 pandemic. Here, we present the influenza surveillance data we retrieved as part of the FluCov project in combination with the survey outcomes and discuss the overall impact the pandemic has had on influenza surveillance including the quality of surveillance data and the likely future of influenza surveillance.
| METHODS The FluCov project covers 22 countries distributed over the American (Canada, Brazil, Mexico, and the USA), African (South Africa), European (France, Germany, Italy, Israel, Netherlands, Poland, Spain, and the United Kingdom), Eastern Mediterranean (Egypt), South East Asian (India and Thailand), and Western Pacific regions (Philippines, Australia, China, Vietnam, Japan, and South Korea). Countries were chosen based on their geographical spread, the presence of a NIC, and the consistent reporting of data to FluNet. In the participating countries, 36 individual NICs were located. As part of this project, we conducted a survey and publish a monthly epidemiological bulletin. These bulletins have been published since June 2021 and provide an overview of the number of positive cases of influenza and SARS-CoV-2 and the percentage of specimens testing positive from January 2019 onwards.
| Surveillance data extraction Influenza data on weekly case numbers and number of specimens taken were extracted from FluNet. 24 The data are provided by NICs and other national influenza reference laboratories collaborating actively with GISRS or are uploaded from WHO regional databases. Data were extracted from the FluNet platform at the beginning of each month since June 2021, and historical data were extracted from the 1st of January 2019 onwards. Data from this platform are regularly updated and sometimes retrospectively corrected.
| Survey results Between November 2021 and March 2022, we retrieved contact details and approached 30 different NICs in the participating countries to complete the NIC survey. Of these, we received 18 responses (response rate: 60%) from NICs in 14 countries. For the Netherlands, we received two responses from the two institutes in charge of influenza surveillance. Officially, their combined efforts form one NIC-but in this paper, we have considered their responses separately. Participating countries are shown in Figure  2 , and their characteristics can be found in Table  1. Five of these countries are low-or middle-income countries (LMICs), and the other nine countries are all high-income countries (HICs) according to the World Bank income classification. 28  these 18 responses, 17 indicated to report their influenza data to the FluNet database and as such completed the influenza-related section of the questionnaire.
Not only were NICs unable to provide continued surveillance efforts as a consequence of a higher work burden and redistribution (or lack) of resources (e. g. , personnel and materials), NPIs in the form of movement restrictions or stay-at-home orders have also affected people's behavior or their ability to access care. 1  This means that the lower influenza case counts, albeit largely caused by limited influenza circulation, could have also been caused by changes in the structure of the surveillance system  33  or decreases in syndromic consultations. 1  These factors should be considered when interpreting influenza surveillance data that were collected during the SARS-CoV-2 pandemic and may affect the reliability of the data presented through FluNet.
Surveillance for SARS-CoV-2 Ten of the NICs participating in the survey shared their SARS-CoV-2 surveillance data with the WHO, four of which are located in LMICs. These NICs were invited to fill out the second part of our survey. The majority of the NICs started reporting SARS-CoV-2 data as of early 2020, with only one NIC fulfilling this role as of January 2021. Most NICs (70%, 7/10) reported both diagnostic and sequencing data and all of them uploaded their sequencing data to the SARS-CoV-2 section of GISAID. The sources of the samples that the NICs tested for SARS-CoV-2 can be found in Table.
ACKNOWLEDGMENTS We would like to thank  Adam Meijer  and  Li Wei Ang  as well as members of the  Global Influenza Program at the WHO  for testing and commenting on an early version of the  NIC survey. On behalf of Italy , we would like to thank  Marzia Facchini ,  Giuseppina Di Mario ,  Angela Di Martino ,  Concetta Fabiani ,  Laura Calzoletti , and  Paola Stefanelli.
3. 2. 5 | 25 Surveillance for SARS-CoV-2 Ten of the NICs participating in the survey shared their SARS-CoV-2 surveillance data with the WHO, four of which are located in LMICs. These NICs were invited to fill out the second part of our survey. The majority of the NICs started reporting SARS-CoV-2 data as of early 2020, with only one NIC fulfilling this role as of January 2021. Most NICs (70%, 7/10) reported both diagnostic and sequencing data and all of them uploaded their sequencing data to the SARS-CoV-2 section of GISAID. The sources of the samples that the NICs tested for SARS-CoV-2 can be found in Table.
Leishmaniasis is a detrimental disease causing serious changes in quality of life and some forms lead to death. The disease is spread by the parasite Leishmania transmitted by sandfly vectors and their primary hosts are vertebrates including humans. The pathogen penetrates host cells and secretes proteins (the secretome) to repurpose cells for pathogen growth and to alter cell signaling via host-pathogen Protein-Protein Interactions (PPIs). Here we present LeishMANIAdb, a database specifically designed to investigate how Leishmania virulence factors may interfere with host proteins. Since the secretomes of different Leishmania species are only partially characterized, we collected various experimental evidence and used computational predictions to identify Leishmania secreted proteins to generate a user-friendly unified web resource allowing users to access all information available on experimental and predicted secretomes.
In addition, we manually annotated host-pathogen interactions of 211 proteins, and the localization/function of 3764 transmembrane (TM) proteins of different Leishmania species. We also enriched all proteins with automatic structural and functional predictions that can provide new insights in the molecular mechanisms of infection. Our database, available at https://leishmaniadb. ttk. hu may provide novel insights into Leishmania host-pathogen interactions and help to identify new therapeutic targets for this neglected disease.
Notably, SLiMs also play a key role in maintaining housekeeping processes in Leishmania, therefore to find candidate SLiMs that may alter the host regulation, we need to discriminate SLiMs of proteins that reach the host cytoplasm or nucleus but limited information about these proteins are available. Currently the only publicly available database dealing with Leishmania proteins is TriTrypDB Shanmugasundram et al. (2023) , which is part of the VEuPathDB Amos et al. (2022). TriTrypDB is a functional genomic resource for Trypanosomatidae, offering proteomic datasets, however it does not focus on protein structure, protein motif search and interactions. We developed LeishMANIAdb to expedite Leishmania research by unifying scattered information from the literature in a user-friendly way and to extend available resources by adding protein level information.
Thereby highly expanded gene families can be directly mapped to functions critical for these parasites Jackson et al. (2016). In this case we could discriminate between proteins with already many paralogs within kinetoplastids and Leishmania-exclusive amplified proteins. When we searched for homologs of Leishmania infantum proteins, we found distinct amino acid transporter and cofactor families already expanded in all kinetoplastids including Leishmania. In contrast, amastins, leishmanolysin, 3'A2-related proteins, kinase-containing putative receptor proteins (and several uncharacterized proteins) seemed to be highly abundant in Leishmania proteomes compared to all kinetoplastids (Figure  1/B ). Comparing complete proteomic datasets yielded only a small overlap. We defined 1) Leishmania_novelty proteins, which are proteins without close homologs in SwissProt, without characterized Pfam domains, and expanded in Leishmania infantum (compared to other kinetoplastids); 2) abundant proteins, which are proteins showing increased abundance upon infection; 3) secreted proteins experimentally identified in at least two secretome experiments.
The LeishMANIAdb web resource To visualize all the collected and calculated information we developed an open-access resource. In LeishMANIAdb users can search for proteins using their UniProt Accession (AC), Entry name (formerly ID), gene name and protein name. We also provide several protein sets as examples to help users browsing the database. Currently proteins are sorted based on 1) species: L. braziliensis, L. donovani, L. infantum, L. major, and L. mexicana; 2) manual curation data; 3) experimental data: secreted proteins, protein abundance/mRNA level data, proteins with any kind of experimental data listed above; 4) computationally predicted information: proteins expanded in Leishmania (score >= 0. 8 -see Methods, Supplementary Material), transmembrane proteins, proteins with high disordered content (at least 70% predicted disorder), proteins with high-scoring linear motifs (score >= 0.
Comparison with other resources In the past decades several databases were built to investigate Leishmania, however they are un- In LeishMANIAdb our main goal was to include protein information relevant to the infection and to complement previously established and still available resources. We included several proteomic datasets, and enriched experimental information with state-of-the-art prediction tools. Still, the most powerful way to explore uncharted proteomes is to inspect MSAs and check for conserved residues and regions -LeishMANIAdb contains precalculated alignments for all proteins. We also added hundreds of annotations to thousands of proteins, including localization and interaction information. While several databases seem to be shut down after a couple of years, our laboratory hosts several resources and we routinely update them.
Website design The LeishMANIAdb website is written in PHP (v8. 0) using the Laravel (v9. 19) framework. All downloaded, predicted or calculated data are stored in a local MySQL (v8. 0) database. To visualize sequence features over amino acid sequences, we developed a javascript package using React (18. 2), while 3D structures are visualized using the original (for non-TM proteins) or a locally modified ver-.
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  101028908 ;  Ministry of Innovation and Technology of Hungary  from the  National Research, Development and Innovation Fund  [ K132522 ]. The authors would like to thank the help of the  Gibson team  at the  European Molecular Biology Laboratory , especially  Jesus Alvarado-Valverde  for helpful suggestions and discussions, and  Philippe Esterre  ( Pasteur Network, Institut Pasteur, Paris ) for his help in selecting a number of Leishmania interactions to be curated.
unusual mechanism to deliver proteins to the cytosol of infected cells by releasing exosomes into the parasitophorous vesicle, which might fuse with the vesicular membrane to release their protein content Silverman et al. (2010). Therefore computational methods based on signal peptides and localization predictions are not sufficient to predict Leishmania secretomes. To overcome this limitation we also used high-throughput experiments Silverman et al. (2008); Cuervo et al. (2009); Hassani et al. (2011); Forrest et al. (2020); Pissarra et al. (2022) to increase the coverage of Leishmania secretomes. Strikingly, the number of proteins in these secretomes varies to a large extent, and some proteins cannot be identified by mass spectrometry. Other datasets include proteins found in glycosomes Jamdhade et al.
We used different methods to predict the structural features of proteins. Classical sequence-based methods can detect globular domains Paysan-Lafosse et al. (2023), TM regions Dobson et al. (2015) and intrinsically disordered regions (IDRs) Erdős et al. (2021). However, the use of AlphaFold2 (AF2) Jumper et al. (2021) provides alternative ways to obtain structural information. In LeishMANIAdb we used structures available in the AlphaFold database Varadi et al. (2022b) (however we could not find 3192 proteins ( 6% of all Leishmania proteins in LeishMANIAdb)). We not only displayed the predicted 3D structure of the proteins, but also information derived from the AF2 models such as the secondary structures and the position of the lipid bilayer for membrane proteins using the method introduced in the TMAlphaFold database Dobson et al.