dataesr/scientific-paragraphs-categorization huggingface dataset:
To further improve data quality, aside from increasing the number of sensors, more advanced techniques can be employed, such as homogenous field correction [42] and source reconstruction techniques, such as beamforming [43], to suppress signals of non-neural origin. By using a larger sample size than employed by previous studies, the current study was able to accurately estimate group-level correspondence between sensor types, as well as characterize sources of variability. The current results show a promising progression towards a high-quality, versatile MEG system without the drawbacks of conventional cryogenic SQUID-MEG. Future efforts will be focused on the implementation of a lightweight whole-head system, which will allow for greatly improved potential for noise reduction as well as extended head coverage.
Research on marine microbial communities is growing, but studies are hard to compare because of variation in seawater sampling protocols. To help researchers in the inter-comparison of studies that use different seawater sampling methodologies, as well as to help them design future sampling campaigns, we developed the EuroMarine Open Science Exploration initiative (EMOSE). Within the EMOSE framework, we sampled thousands of liters of seawater from a single station in the NW Mediterranean Sea (Service d'Observation du Laboratoire Arago [SOLA], Banyuls-sur-Mer), during one single day. The resulting dataset includes multiple seawater processing approaches, encompassing different material-type kinds of filters (cartridge membrane and flat membrane), three different size fractionations (>0. 22 µm, 0.
22-3 µm, 3-20 µm and >20 µm), and a number of different seawater volumes ranging from 1 L up to 1000 L. We show that the volume of seawater that is filtered does not have a significant effect on prokaryotic and protist diversity, independently of the sequencing strategy. However, there was a clear difference in alpha and beta diversity between size fractions and between these and "whole water" (with no pre-fractionation). Overall, we recommend care when merging data from datasets that use filters of different pore size, but we consider that the type of filter and volume should not act as confounding variables for the tested sequencing strategies. To the best of our knowledge, this is the first time a publicly available dataset effectively allows for the clarification of the impact of marine microbiome methodological options across a wide range of protocols, including large-scale variations in sampled volume.
Additionally, variables such as the shape and life cycle stage of protists can result in the identification of the same species across different size fractions, as observed for diatoms  [39]. Within the frame of the EMOSE 2017 initiative, we sampled water from a coastal site of the NW Mediterranean Sea (SOLA), and constructed a unique dataset of deeply-sequenced metagenomes and 16 S/18 S rRNA gene amplicons (MetaB16SV4V5 and MetaB18SV9). To our knowledge, this dataset represents the largest sequencing effort ever conducted at a single site on a single day. The experiment was designed to compare different filtration volumes, filter types, size fractionations and sequencing strategies earlier used in some of the most relevant global ocean initiatives, i. e. , Tara Oceans  [10] , Malaspina  [9] , and the OSD  [13].
2. 3  [50]  and SILVA database v132  [51]. For shotgun metagenomics data, mOTU2  [52]  were used with SILVA database v132  [51]  for taxonomic lineages. The number of reads at each step, for each sample and the respective accession numbers and links are available in Supplementary Table  S3. Despite the recent revision in the taxonomy of microorganisms at the phylum level, and hence substantial nomenclature modifications  [53] , we used the taxonomic information as provided by the MGnify platform after the SILVA database v132  [51].
Abundance tables pre-processing The tables with abundance per taxonomic lineage and sample were directly transferred from the MGnify platform to the R software environment v3. 6. 3  [54]. We started by dividing the sequencing runs into prokaryotes and protists. Specifically, the MetaB16SV4V5 was filtered to include taxonomic lineages attributed to prokaryotes. We removed any taxonomic lineages attributed to organelles (mitochondria and chloroplasts). Using the same reasoning, for the MetaB18SV9 approach, we focused on protists instead and excluded any taxonomic lineages assigned to prokaryotic, metazoan, fungi, or viridiplantae taxonomy. Metagenomic data were subdivided into a prokaryotic dataset and a protist dataset, because sequencing all DNA without primer bias allows to identify either biological groups.
Statistical analysis All statistical analyses were performed in the R software environment v3. 6. 3  [54]. Alpha and beta diversity metrics were calculated using the vegan v2. 5. 7 package  [55] , all figures, except Fig. 1 , were produced with the ggplot2 v3. 4. 0 package  [56]  or base R. Statistical tests and their assumptions were tested using the rstatix v0. 7. 0 package  [57]  and followed the guidelines for best practices proposed in  [57]. The alpha diversity metric used was the total number of taxonomic lineages in a given sample, i. e. , species richness. We decided to use a single alpha diversity metric to simplify readability of the results and selected species richness because it is the most straightforward alpha diversity metric. This alpha diversity metric allows us to assess the direct output of the methodologies compared, making it the most general purpose one.
The tests were performed for comparisons with at least three replicate samples per independent group. For each test, we specified if the p value was significant or not (alpha = 0. 05), after adjusting with the Bonferroni method for multiple comparisons  [61]. To describe beta diversity, we compared the dissimilarity between methodological variables. For that purpose, we used Bray-curtis dissimilarity matrices and visualized the distance between samples with ordination plots, specifically nMDS, with the metaMDS function of the vegan package  [55]. Significance values were calculated by PERMANOVA with adonis2 function and homogeneity of variance was verified with betadisper function, both from the vegan package  [55]. Finally, significance of distance to centroid was accessed with Tukey test, with base R functions.
Strengths and limitations of the EMOSE dataset This dataset is publically available (see Data Availability section) and contains samples from more methodological variables than the ones presented in this work. For example, we did not use the samples from amplicon sequencing results of the 16 S rRNA gene divided by reads length at the library preparation stage. Another variable which we did not consider was the sequencing machine (HiSeq or MiSeq). A recent study compared replicates from HiSeq and MiSeq platforms, after 16S rRNA gene amplicon sequencing, and found differences in community composition  [69]. We note, however, that the aforementioned study focused on the coral microbiome, while ours is focused on seawater microbial communities. Due to the high number of different variables tested in this study, the EMOSE dataset has hundreds of samples, which is a significant advantage over up-to-date studies, but also has some limitations.
Firstly, it might be complex to use the data available due to the high number of variables. Secondly, the high number of samples is a result of many variables tested, not of many replicates (n = 3, wherever possible). However, it should be noted that keeping the same number of replicates across all variables was not possible, due to on-site methodological and logistic impossibilities, including the effort and time needed to filter 100 L of seawater using three different filters. Some limitations are all the more understandable when we take into account the fact that, as far as we know, the presented dataset assesses the effect of filtration of several thousands of liters of seawater carried out for the first time on such a scale during a one-day/one-place campaign. 5 L (single filter) and 10 L (four 2.
DATA AVAILABILITY All raw sequences from the EMOSE dataset, which includes all data used in this article, are available in the European Nucleotide Archive under the accession number ERP090011. The abundance tables are available at MGnify platform under accession number MGYS00001935. Note that both Versions 5 and 4. 1 include all sequencing strategies (MetaB16SV4V5, MetaB18SV9 and metagenomes). The metadata for each sample database was recorded in PANGEA  [71] , but a cleaner version is available at Supplementary Table  S2. The R scripts for all data manipulation, statistical tests and figures is available in github (https://github. com/pascoalf/Inter-comparison-ofmarine-microbiome-sampling-protocols), no manipulation of data was done outside the scripts provided, except for manual curation of taxonomy, because it needs human experts' evaluation.
FUNDING This study was supported by the  Euromarine European Marine Research Network  by funding the initiative  EMOSE 2017  " Inter-Comparison of Marine Plankton Metagenome Analysis Methods ". The  Portuguese Science and Technology Foundation (FCT)  funded this study through the grants  PTDC/CTA-AMB/4946/2020 ,  2022. 02983. PTDC ,  2020. 03139 CEECIND  to CM and a PhD grant to FP ( 2020. 04453 ). The work of LT was undertaken, in part, thanks to funding from the  Canada Research Chairs program  and a  Discovery Grant  from  NSERC. This study was also partially funded by the  Strategic  Funding  UIDP/04423/2020 ,  UIDB/04565/2020  and  LA/P/0140/2020  through national funds provided by  FCT  and carried out under the projects  ATLANTIDA  (ref. NORTE-01-0145-FEDER-000040 ) and  Ocean3R  ( NORTE-01-0145-FEDER-000064 ), supported by the  Norte Portugal Regional Operational Program  ( NORTE 2020 ), under the  PORTUGAL 2020 Partnership Agreement  and through the  European Regional Development Fund (ERDF).
Importantly, the poly(I:C)-induced PD callose deposition was drastically reduced in serk1-1 mutants compared to WT Col-0 plants (Figure  3D  and E ). Whereas the median PD callose level increased by 48% in WT col-0 plants upon poly(I:C) treatment, only a minor increase in the median PD callose level was observed in the serk1 mutant. Thus, the data indicate that SERK1 contributes to poly(I:C)-induced BIK1 phosphorylation and may function genetically upstream of BIK1. To investigate the poly(I:C)-induced signaling pathway downstream of BIK1, we examined the production of reactive oxygen species (ROS) by a luminescence assay. ROS play important roles in plant development and stress responses  (Mittler, 2017)  and are also produced during infections with fungal and bacterial pathogens  (Castro et al.
Analysis of virus infection in the presence of elicitors To test the effect of elicitors on TMV-GFP infection in N. benthamiana, plants were inoculated with 200 µl inoculum containing 20 µl of infectious viral RNA transcription mix and 0. 5 µg/μl (equals ~1 µM) poly(I:C) (Sigma-Aldrich, USA), or 1 uM flg22 (EZBiolabs, USA or Proteogenix, France), or water. Infection sites on the inoculated leaves were imaged using a hand-held camera and UV lamp (BLAK RAY B-100AP; UVP Inc. , Upland, California) in the presence of a ruler for size normalization. The areas of infection sites in each leaf were measured with Image J software upon selection of infection site as regions of interest using fluorescence thresholding and the wand tracing tool, and by setting the scale according to the ruler. For testing the effect of poly(I:C) on viral replication, N.
3 software (Carl Zeiss, Jean, Germany) and using a 405 nm diode laser for excitation and filtering the emission at 475-525 nm. 8-bit Images acquired with a 40× 1. 3 N. A. Plan Neofluar objective with oil immersion were analyzed with ImageJ software (http://rsbweb. nih. gov/ij/) using the plug-in calloseQuant, which after setting few parameters localizes fluorescent callose spots and quantifies callose fluorescence intensity of each spot automatically  (Huang et al. , 2022). This plugin is available at https://raw. githubusercontent. com/mutterer/callose/main/calloseQuant_. ijm. Callose spots.
Imaging Microscopical imaging was performed with a Zeiss LSM 780 confocal laser scanning microscope equipped with ZEN 2. 3 software (Carl Zeiss, Jean, Germany). Excitation / emission wavelengths were 405 nm/475-525 nm for aniline blue, 488 nm / 500-525 nm for GFP, and 561 nm/560-610 nm for RFP. dsRNA is imaged through binding of the Flock house virus B2 protein fused to GFP (B2:GFP). In cells of zone I (non-infected cells ahead of infection) B2:GFP shows a nucleo-cytoplasmic distribution, which is the typical distribution of this protein in the absence of dsRNA  (Monsion et al. , 2018). In cells at the virus front (zone II), B2:GFP co-localizes to MP:RFP to spots at the cell wall      Figure Legends.
C reduces the induction of PD callose deposition in the presence of poly(I:C). Leaf disks excised from the RFP (control) or MP:RFP-expressing leaves 48h after agroinfiltration were incubated for one day in water, then transferred into aniline blue solution with and without 0. 5 µg/µl poly(I:C) and imaged after 30 minutes. For each treatment, three images of three leaf disks taken from three plants were analyzed for PD-associated callose levels. RFP data are combined data from the 27 leaf disks that were used as RFP control in the individual agroinfiltration experiments. The increase in poly(I:C)induced PD-callose levels seen in the presence of poly(I:C) as compared to the water-treated control is shown in percent (%). Two-tailed Mann-Whitney test; ****, p = <0. 0001; ***, p = 0. 0002.
Acknowledgements We acknowledge funding from the  Agence National de la Recherche  (Plant- KBBE2012 / ANR-13-KBBE-0005-01 ,  ERA-NET SusCrop2 / ANR-21-SUSC-0003-01 ;  ANR-PRC / ANR-21-CE20-0020-01 ) and the  Chinese Scholarship Council  (PhD fellowship for CH) to MH, and funding from the  NIH  ( R35GM144275 ),  NSF  ( IOS-2049642 ), and the  Robert A. Welch Foundation  ( A-2122-20220331 ) to LS. We thank  Cyril Zipfel ,  Christine Faulkner ,  Matthew Gilliham ,  Kenichi Tsuda ,  Christophe Ritzenthaler , and  Yoselin Benitez-Alfonso  for providing seeds of Arabidopsis mutants and transgenic lines. We also thank  Minna Poranen  ( University of Helsinki, Finland ) for providing Phi6 dsRNA.
Scale bar, 20 µm. (C) Expression of either MP TMV :RFP, MP ORMV :RFP, or MP TVCV :RFP strongly reduces the induction of PD callose deposition in the presence of poly(I:C). Leaf disks excised from the RFP (control) or MP:RFPexpressing leaves 48h after agroinfiltration were incubated for one day in water, then transferred into aniline blue solution with and without 0. 5 µg/µl poly(I:C) and imaged after 30 minutes. For each treatment, three images of three leaf disks taken from three plants were analyzed for PD-associated callose levels. RFP data are combined data from the 27 leaf disks that were used as RFP control in the individual agroinfiltration experiments. The increase in poly(I:C)-induced PD-callose levels seen in the presence of poly(I:C) as compared to the water-treated control is shown in percent (%).
Any sensitive data stored in the file will be masked and not available for any unauthorized users; furthermore, authorized users will have to use the key to unmask the sensitive information after retrieving it from the cloud  [9]. In the Cloud-RAID model, the user data is first encrypted and distributed across cloud storage providers using Redundant Array of Independent Disks (RAID) technology to achieve confidentiality and availability of the user data  [10, 11]. Bandwidth and Maintenance minimum Cloud system (BMCloud) was developed using E-Code and JUDGE_STYLE algorithms. This system uses low bandwidth during recovery and also tries to address the issues related to consistency and storage cost  [12]. The SaveMe model combines both the public and private cloud storage and provides security from the client-side.
Cloud computing, however, requires security algorithms that allow a linear search of deciphered data that guarantees the security of the data  [28]. A robust security approach using GA has been introduced for cloud data security. It is simple and easy to implement having only two main processes of crossover and mutation  [29]. A Cloud storage system is a storage system over a large scale that consists of many independent storage servers. During recent years a huge change and adoption have been seen in cloud computing so security has become one of the major concerns in it. As Cloud computing works on a third-party system so security concern is there not only for customers but also for service providers. This paper introduces an innovative technique called the Virtual Cloud Storage Architecture (VCSA) model to store and retrieve the data economically and securely using multiple clouds.
In the proposed architecture, the storage of a file in multiple clouds has to go through four layers of tasks: encryption, encoding, hashing, and allocation and retrieval of the file have to pass through the same steps in reverse order. For storing the file in multiple clouds using the VCSA approach, the file was passed to the frontend cloud system and the complete file was encrypted using the auto-generated key. Then the encrypted file was encoded into different chunks using the simple regenerating codes. Next, the encoded chunks were hashed separately using the MD5 algorithm, and each chunk was allocated to one of the available private or public cloud storage using the newly proposed Circular Shift Chunk Allocation (CSCA) algorithm. The retrieval process obtained the metadata about the file and then applies the CSCA algorithm to locate the chunks from different cloud storage.
The collected chunks were hashed separately and if any of the chunks were found missing or tampered with then, it was recovered using the regenerating code. The chunks were aggregated together to form a single file using the decoding process and finally, the file was decrypted to obtain the actual file. The performance of this innovative VCSA technique was fair enough when compared to the other techniques in terms of cost, time, and extended level of security. The main aim of this paper is to ensure distributed cloud data storage security to fulfill the CIA (Confidentiality, Integrity, and Availability) model. The architecture presented in this paper is easily scalable to any number of clouds and for files of any size. The availability feature of this system ensures the regeneration of chunks depending upon the number of parity chunks.
Virtual Cloud Storage Architecture The VCSA model is a novel approach that enables users to store their files securely in cloud storage without worrying about its security. The model integrates multiple public clouds in the background to provide a single virtual storage system for the users. The model splits the file into chunks and stores it in different clouds and can be collected and reorganized only by the user. Many of the cloud storage systems may fail or not be available, but this model helps to recover the file chunks that are even lost. The VCSA model uses 4 levels of operation to ensure the security of the data during storage and retrieval. In the first layer, a front-end private cloud system gets the user file and it is encrypted using the auto-generated key. In the second layer, the encrypted file is encoded into different chunks using the Simple Regenerating codes.
System Model The Virtual Cloud Storage Architecture provides a simple and efficient means to store and retrieve data securely in private or public cloud storage. Fig. 1  depicts the overall architecture of the proposed VCSA model that has two major modules: file storage and retrieval. VCSA front-end uses a private cloud running with a minimum storage capacity. After successful login, the user can perform four file operations such as upload, download, list, and delete. When the user uploads a file, only the metadata will be stored in the private cloud and the data chunks are stored in different public clouds. The authentication details of all public clouds are also stored in the private cloud. This architecture ensures the confidentiality and secrecy of the stored data using the four layers of security which is impossible for any hackers to crack.
File Retrieval Module The file is stored in multiple clouds after passing through the four layers of operations. Retrieving the complete file from multiple clouds has to pass through the reverse operations of file storage as shown in Fig. 3. The cloud interface shows the list of files available in the cloud storage. Once the user selects the file to be retrieved, the cloud service collects and integrates all the required chunks from the clouds based on the metadata. The metadata contains the file details like file name, file size, chunk name, and a message digest of each chunk. If any of the required chunks are not available or tampered with, the VCSA model will rebuild them using the remaining chunks. The following steps clearly explain the file retrieval process. The order in which the chunks are stored in the clouds varies from file to file based on the chunk size, and this gives an extra level of security to the user files.
Response Time The performance of the VCSA model is evaluated based on the time to perform both the storage and retrieval process. The response time is the total time taken for the model to completely store the file or retrieve the file back from the cloud storage without any rebuilding. The following Fig. 5  shows the comparison of response time for the storage and retrieval process using the VCSA model. From the chart, it is evident that the retrieval process takes comparatively less time than the storage process. The main reason for the variation in response time is as follows: during the encoding operation, the file is split into data and parity blocks, but during the decoding operation, the parity chunks don't get downloaded. The second inference from the chart is that the response time for the file retrieval process is almost flat and has less variation for the larger files.
Similar to NaDPA powder, the amount of CaDPA in the spores contributing to the CARS signal can also be estimated. On average, a single spore of B. atrophaeus contains 49 fg of CaDPA salt in the core, because the mass of a dry spore is 328 fg  43  , and the relative mass of CaDPA is generally assumed to be around 15%. As the mean volume of B. atrophaeus spores is expected to be of 0. 27 μm 343 , we assumed an effective volume of 1 μm 3 for the close packed spores of our deposited sample; according to this, the number of spores contained within the volume probed by the laser beam ( 1. 4 × 10 6 μm 3 ) is estimated to be 1. 4 × 10 6 , corresponding to 70 ng of CaDPA contributing to the CARS signal. According to Fig. 4E , this provides a SNR of 20 on the main peak at 1018 cm -1 , which is in close agreement with the data extrapolated from NaDPA results.
Sample collection The individuals of self-reported African (AFB) and European (EUB) descent studied are part of the EVOIMMUNOPOP cohort  18. In brief, 390 healthy male donors (188 AFB and 202 EUB) were recruited between 2012 and 2013 in Ghent (Belgium), thus, before the COVID-19 pandemic. Blood was obtained from the healthy volunteers, and the PBMC fraction was isolated and frozen. Inclusion in the current study was restricted to 80 nominally healthy individuals of each ancestry, aged between 19 and 50 years at the time of sample collection. Donors of African descent originated from West Central Africa, with >90% being born in either Cameroon or the Democratic Republic of Congo. For this study, 71 additional individuals of East Asian descent (ASH) were included, of whom 62 were retained after quality control (see the 'scRNA-seq library preparation and data processing' section).