dataesr/scientific-paragraphs-categorization huggingface dataset:
Nevertheless, the acquisitions require that the user is well trained to perform experiments in a reproducible manner. A future direction will be to establish a single sample slide and the adapted software for an automated acquisition, eliminating associated human error here. Acquired data needs to be analyzed in a reproducible manner as well. We thus developed and proposed the ImageJ/Fiji plugin MetroloJ_QC. When possible, we compared the measured values with the theoretical ones, such as the resolution for PSF or the coregistration. For some other cases, we referred to manufacturers' specifications like the stage repositioning or the sensor read noise. We also associated the limit values summed up in Table  1  with the limits for biological experiments when possible. We gave recommended measurement frequency, which depends on multiple parameters like the per year occupation time of the microscope, the number of users (major difference if the system is in a core facility with dozens or hundreds of users per year or in a research team, used by the same people the whole year), the human resources that take care of the microscope, and the kind of biological experiments carried out.
It should be noted that the datasets of that work were not acquired to compare the performance of different systems. This kind of comparison may be influenced by parameters difficult to standardize, like the microscope environment or the association of the different microscope components which are by nature different, even if they can have similar datasheets, and thus lead to different results. Our datasets and the resulting guidelines aim to characterize the microscope's health state over time, using relative and not absolute values. We consider that the metrics that we define in that work complement the ISO 21073:2019 norm (ISO, 2019) and hopefully will be further enriched by the international initiative QUAREP-LiMi, whose role is to establish commonly accepted guidelines for quality assessment in light microscopy  (Boehm et al.
, 2021; Nelson et al. , 2021). This need for standardization accepted by the whole scientific community and principal actors from the academic and private sectors has been recently highlighted  (Marx, 2022a (Marx, , 2022b)). The recording of the metric values is the last link in the chain of the QC. Monitoring over time the different measured values is necessary, and a simple spreadsheet file may be sufficient. Ideally, the solution is a QC management database for processing the analysis and obtaining the metrics as quality control images and data are imported  (Huisman et al. , 2020; Linkert et al. , 2010; Swedlow et al. , 2021). Furthermore, such a database would be able to integrate these values into the metadata of the acquired images  (Hammer et al. , 2021). In the end, these metrics would allow an open and accessible inspection of the quality of the used microscope and of every single image, within a collection of microscopy metadata, as recently proposed by  Rigano et al.
Acknowledgments The authors thank  Sebastian Beer  and  Bruno Emica  ( Hamamatsu Photonics ) and  Gerhard Holst  ( Excelitas PCO GmbH ) for their valuable support and discussion on the metrics of the QC of camera sensors. We would like to thank  Kees van der Oord  (Nikon Europe B. V. ),  Christian Schulz  ( Leica Microsystems CMS GmbH ), and  Ralf Wolleschensky  ( Carl Zeiss Microsocpy GmbH ) for their helpful input on PSF issues, and  Arnd Rühl  ( Märzhäuser Wetzlar GmbH ) for his invaluable advice on the stage part of this manuscript. We thank  Yea-lih Lin  for the help on Fig. 1 and  Azam Aslemarz ,  Nathalie Morin ,  Raissa Rathar ,  Daniel Bouvard , and  Maritzaida Varela  for providing us with the biological samples for the QC metrics validation. We thank  Claire Dupont  for the.
Data availability The download link of the plugin, direct access to its dependencies, its manual, and simple sample preparation and acquisition protocols can be found at: https://github. com/ MontpellierRessourcesImagerie/MetroloJ_QC. We provide the majority of the datasets used for this work, with the maximum of metadata available under the principle of accessibility of research open source data compatible with the FAIR principle. The data represent 360 GB of data and are accessible under the following DOI: https://doi. org/10. 57889/OD_METROLOJ_QC.
Effective crop improvement, whether through selective breeding or biotech strategies, is largely dependent on the cumulative knowledge of a species' pangenome and its containing genes. Acquiring this knowledge is specially challenging in grapevine, one of the oldest fruit crops grown worldwide, which is known to have more than 30,000 genes. Well-established research communities studying model organisms have created and maintained, through public and private funds, a diverse range of online tools and databases serving as repositories of genomes and gene function data. The lack of such resources for the non-model, but economically important, Vitis vinifera species has driven the need for a standardised collection of genes within the grapevine community. In an effort led by the Integrape COST Action CA17111, we have recently developed the first grape gene reference catalogue, where genes are ascribed to functional data, including their accession identifiers from different genome-annotation versions (https://integrape.
eu/resources/genes-genomes/). We present and discuss this gene repository together with a validation-level scheme based on varied supporting evidence found in current literature. The catalogue structure and online submission form provided permits community curation. Finally, we present the Gene Cards tool, developed within the Vitis Visualization (VitViz) platform, to visualize the data collected in the catalogue and link gene function with tissue-specific expression derived from public transcriptomic data. This perspective article aims to present these resources to the community as well as highlight their potential use, in particular for plant-breeding applications.
Maize Genetics and Genomics Database (maizeGDB)  (Woodhouse et al. , 2021). Although some resources are available for grapevine, there is plenty of room for improvement. The first release of the highly homozygous Vitis vinifera cv. PN40024 reference genome assembly  (Jaillon et al. , 2007)  and its subsequent annotation updates, including 12X. CRIBI-V1 and 12X. VCost. v3 (Canaguier et al. , 2017) , represent hallmarks in the grapevine field as they paved the way for gene characterisation studies at the genome level. Moreover, there have been key advances in assemblies of contiguous and phased diploid genomes of grapevine cultivars of greater agricultural importance such as cv. "Cabernet Sauvignon"  (Chin et al. , 2016; Minio et al. , 2019b) , cv. "Carménère"  (Minio et al. , 2019a) , and even clones of cv.
The highest validation level, six, corresponds to the same type of experimental validation as level five but FIGURE 1 | Publication trends of gene and gene family characterisation studies in grapevine. The PN40024 genome assembly and annotation releases  (Jaillon et al. , 2007; Canaguier et al. , 2017) , the first genome-assisted gene family study  (Matus et al. , 2008) , and the phased diploid Cabernet Sauvignon genome assembly  (Chin et al. , 2016)  are included as milestones in grape genomics. Data were collected from NCBI using three different queries for each category. in Vitis vinifera. Biochemically validated enzymes in other species such as Escherichia coli or Nicotiana benthamiana are also given the maximum level of validation, as same-species validation is considered impractical and unnecessary.
Cabernet Sauvignon  (Minio et al. , 2017). Conversions between PN40024 and CS08 Cabernet Sauvignon gene IDs were obtained from a conditional reciprocal best BLAST using the CRB-BLAST software with default parameters  (Aubry et al. , 2014). This can be seen as the first step toward a future pangenome version of the catalogue, which would connect published gene function data to new and updated genome assemblies from different cultivars, as well as the current PN40024 reference. There is a noticeable gap between the ≈2,000 genes in the catalogue and the 41,733 protein coding genes in Vitis vinifera according to VCost. v3  (Canaguier et al. , 2017). However, this gap is suspected to be somewhat smaller according to recent proteincoding gene estimates for the new PN40024 genome assembly (40X. v4), which are around 35,000 genes suggesting that the VCost.
GENE CARDS Gene Cards is an app developed within the Vitis Visualization (VitViz) platform 2 : a suite of bioinformatic tools working on upto-date Vitis vinifera datasets Gene Cards integrates catalogue information with RNA-Seq transcriptomic datasets providing a biological summary sheet on an individual gene basis. Individual genes can be found through the provided search bar in the form either through their gene symbol, V3 or V1 IDs. The resulting summary includes information from the catalogue and other sources as well as providing an interactive tissue-specific view of gene expression. Information is provided for every annotated gene in the genome, however, a greater level of detail is associated to catalogue genes. The transcriptomic datasets shown in Gene Cards have been derived from public SRA data with the objective of being as comprehensive as possible was used as a first filter to obtain 4,815 runs corresponding to mRNA transcriptome sequencing experiments.
The first search was carried out on the 28th of April 2021 and the number of SRA runs are expected to grow as periodic updates take place. This will be especially important for tissue categories for which there are not as many SRA runs yet. The metadata of these runs was used to perform both automatic and manual sorting into nine different tissue categories; leaf, fruit, flower, bud, tendril, stem, seed, root, and wood. Further filters, applied to the datasets, included removing runs corresponding to mixed tissues, making sure sRNA/ncRNA datasets were removed and setting a minimum of ten million successful alignments per run and four runs per BioProject. A total of 2,737 SRA runs were included in the Gene Cards application. The data are interactively represented in the form of tissue-specific box-plots in which each dot corresponds to a single SRA run, which upon selection displays both the SRA run and BioProject IDs.
resulted in deleting several DR1 sources as well, not only new seeds. More precisely, 4FGL J1640. 6-4632 was replaced by HESS J1640-465, five DR1 and two DR2 sources were deleted in or close to Vela X, three DR1 and one DR2 sources in the new SNR G279. 0+1. 1. We also deleted the faint 4FGL J1814. 1-1710 next to the extended source HESS J1813-178 (even though this one did not change) because its TS decreased from 30 in DR1 to 16 in DR3, and it was not spectrally distinct from HESS J1813-178. Following the catXcheck verification (Section 3. 6), we have applied several modifications to fit the data better. We replaced 4FGL J0857. 7-4507 (in one of the clusters described in Section 6. 2) by a stronger uw1216 source about 0°. 5 away. We replaced 4FGL J1750. 0-3849 just outside the extended source FHES J1741.
6-3917 by a stronger point source just inside it. We have looked at individual faint (TS < 25) sources with a very soft (Γ > 3. 1) spectrum, a large error ellipse (R 95 > 0°. 5), or a stronger close neighbor in uw1216 and deleted three DR1 (4FGL J0533. 9+2838, J0313. 6-7508, and J2326. 5+8555) and three DR2 (4FGL J0750. 0+7140, J1112. 0+1021, and J1752. 2-3002) sources. Nearby seeds appeared in DR3 and fit the data better. Similarly, we have deleted a faint very hard (Γ < 1. 3) DR2 source (4FGL J1830. 3-1601) that was superseded by a brighter nearby DR3 seed. As before we manually added 11 known LAT pulsars that could not be localized by the automatic procedure without phase selection. Only one of those (PSR J1731-4744) reached TS > 25 in DR3. It appears with a NULL error ellipse. The 10 others were discarded.
Thresholding and Light Curves We used the Fermi Tools 1. 4. 7 analysis suite. 91 Compared to the Science Tools v11r7p0 that were used in 4FGL, the main difference is the new edisp_bins setting for energy dispersion, which allows for broadening of the energy interval considered in the model space. The Science Tools setting was equivalent to edisp_bins = -1, not enough to fully fill the first and last data bins for the small energy bins that we use (10 per decade). We used edisp_bins = -2 instead, considering two bins for energy dispersion on each side beyond the data bins. The acceptance for the PSF3 event type increases by a factor 2. 4 between 50 and 100 MeV, so to account for this rapid change, we used six energy bins in the first component (Table  1 ) instead of three and set edisp_bins to -4 accordingly.
The statistical uncertainties on the parameters decreased by 15% at high latitude and 9% close to the plane. The random character of adding new data and the variable nature of the γ-ray sources inevitably lead to broadening the TS distribution, in addition to shifting it to larger values. The sharp selection threshold at TS = 25 in DR1 becomes a tail of sources at TS < 25 in DR3. Figure  12  shows the TS distribution outside the Galactic plane, where 74 DR1 sources have TS < 25. Over the entire sky, 112 DR1 sources have TS < 25 in DR3, among which 15 have TS < 16, and the smallest TS is 9. 8 (4FGL J1816. 4-0057). The energy flux decreased on average by 5% (0. 2σ) between DR1 and DR3 (considering only PL sources to avoid the effect of switching to a curved model). This is again a selection bias dominated by faint sources: close to the detection threshold, DR1 sources can get either brighter or fainter in DR3, but faint sources in DR3 cannot be fainter in DR1 because they would have TS < 25.
Step-by-step from DR1 to DR3 To understand the effects of the analysis changes introduced in DR3 with respect to 4FGL, we have considered their effects on the DR2 data set (10 years). To that end, we started with the same seeds as the DR2 catalog, changed each element in sequence (in the order of the list below), and compared each intermediate result with the previous one. 1. We first switched from the Science Tools to the Fermi Tools, and from the P8R3_V2 to the P8R3_V3 data and Instrument Response Functions (changing the isotropic templates accordingly). This led to essentially no change at all, as expected. 2. Setting edisp_bins to -2 (Section 3. 3) when accounting for energy dispersion had a significant effect. Prior to that, the model counts were underestimated by about 5% in the first energy band (50 to 100 MeV), so the model fit was too high by the same amount at low energy.
This work is performed in part under  DOE  Contract  DE-AC02-76SF00515. This work made extensive use of the ATNF pulsar catalog 102 (Manchester et al. 2005). This research has made use of the NASA/IPAC Extragalactic Database (NED; 2019), which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration , and of archival data, software, and online services provided by the  ASI Science Data Center (ASDC)  operated by the  Italian Space Agency. We used the  Manitoba SNR catalog (Ferrand & Safi-Harb 2012)  to check recently published extended sources. We acknowledge the  Einstein@Home  project for providing new pulsar associations through the dedicated efforts of the Einstein@Home volunteers. The  Einstein@Home  project is supported by the  NSF  award  1816904.
Machine learning has revolutionized every facet of human life, while also becoming more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare, with numerous applications and intelligent systems achieving clinical level expertise. However, building robust and generalizable systems relies on training algorithms in a centralized fashion using large, heterogeneous datasets. In medicine, these datasets are time consuming to annotate and difficult to collect centrally due to privacy concerns. Recently, Federated Learning has been proposed as a distributed learning technique to alleviate many of these privacy concerns by providing a decentralized training paradigm for models using large, distributed data. This new approach has become the defacto way of building machine learning models in multiple industries (e.
Introduction Over the past decade, machine learning has witnessed rapid growth due to the proliferation of deep learning. Fueled by large-scale training databases  [1] , these data driven methods have gained significant popularity. Thanks to rapidly evolving architectures, (e. g. , AlexNet  [2] , GoogLeNet  [3] , ResNet  [4] ) convolutional neural networks (CNNs) have demonstrated consistent improvement on difficult computer vision tasks including classification, object detection, and segmentation. Other areas of machine learning, such as natural language understanding, recommendation systems and speech recognition, have also seen outstanding results in their respective applications through the introduction of novel approaches such as transformers  [5, 6] , DLRM  [7]  and RNN-T  [8]. Such advancements in artificial intelligence and machine learning have already disrupted and transformed healthcare through applications ranging from medical image analysis to protein sequencing  [9] [10] [11] [12].
And yet, while there are over 150 AI-based interventions that are approved by the FDA (an updated list with a focus on radiology can be reviewed at https://aicentral. acrdsi. org), many open questions persist about how to best deploy existing AI solutions in healthcare environments  [13]. In addition to getting existing solutions deployed, there are many challenges that must be overcome during the training process. A consistent bottleneck has been the need for large amounts of heterogeneous data to train accurate, robust and generalizable machine learning models. However, most healthcare organizations rarely carry data in such large quantities, especially in the case of homogeneous populations or rare diseases with scarce amounts of cases. A common way data scientists attempt to overcome this issue is by first pre-training a model on large, generic datasets (e.
g. , ImageNet  [1] ), and then fine-tuning them on specific medical tasks of interest. However, even with this approach, underperformance or generalizability issues  [14]  may persist. This is often the case for medical tasks where there exists a large domain shift between medical data (e. g. , brain MRI, abdomen CT, genomics) and general purpose public datasets such as ImageNet  [1] , MIMIC-CXR  [15] , ChexPert  [16] , etc. More recently, Self Supervised Learning (SSL) approaches have demonstrated promising results in performance using large unlabelled datasets, thus alleviating the need for annotations; however, even with such SSL approaches, the need for access to large amounts of heterogeneous medical data is still necessary to train robust medical ML algorithms  [17, 18]. In addition to large, heterogeneous datasets, the other most common bottleneck for ML algorithm training is computational power.
Federated Learning emerged as a distributed learning paradigm that takes into account several practical challenges, and differentiates itself from traditional distributed learning settings, as noted by Google  [20] , by addressing four main themes: statistical heterogeneity of data across nodes, data imbalance across nodes, limited communication in the distributed network (e. g. , loss of synchronization, variability of communication capabilities), and the possibility of a large number of nodes relative to the amounts of data. In the Federated Learning setting, a "federation" of client sites with their own datasets train models locally and then send their updates to a server. The weights are the only information passed over lines of communication aiming at preserving privacy. The model weights are then aggregated in the server from the client updates, and the resulting aggregated model weights are sent back to the clients for the next round of training.
Federated Learning Overview Federated Learning was first introduced as a decentralized distributed machine learning paradigm by Google  [20]. The standard Federated Learning paradigm that is outlined in this paper is as follows: i) Multiple client sites, each containing a local dataset that remains at the client site during the entirety of training, connect to a global server; ii) A global model is initialized in the global server, and the weights from this global model are passed to each of the local client sites; iii) Each client site trains a local version of the global model on their respective dataset, and then sends the updated model weights to the global server; iv) The global server updates the global model by aggregating the weights it receives from the local clients, and then passes a copy of the updated global model to each of the clients.
The process that occurs between steps i-iv is called a round, and during federated training, steps i-iv are repeated for multiple rounds until the global model converges to a local minima. The most important aspect of this process is step iii. During this step, all data used for training is kept strictly on the local clients' machines. The only information that is passed between the clients and the server are weight updates. This enables multiple sites to pool their data for training of a global model while still maintaining data privacy. During step iv, the authors use an algorithm that they coin federated averaging to aggregate the weights. In this algorithm, each weight updated is weighted by the size of the client dataset from which it comes, relative to the size of the other client datasets.
In Vertical Federated Learning, users are the same across all client sites, but each client sites' data consists of different features, so the same user will be analyzed through different modalities depending on the client site. In Transfer Federated Learning, the client sites don't have users or features in common, but the tasks in their datasets are still marginally related, so pooling them together typically leads to more robust network training. For a more general review of Federated Learning, readers are referred to  [29, 31, 32]. Here we also list common Federated Learning platforms: OpenFL  [33] , PySyft 1  , Tensorflow-Federated 2  , FedML  [34] , Flower 3  , NVIDIA Clara 4  , Personal Health Train (PHT 5  ).
Search Design The literature review was conducted in October 2021 by searching Google Scholar for papers published between 2019 and 2021 that matched the query: federated AND (cancer OR cancers OR tumor OR tumors OR oncology). We chose this time period for our search query due to the fact that Google didn't publish their seminal Federated Learning paper  [35]  until 2017, so we didn't see a large amount of medical applications until than. A visual representation of the split of the material reviewed is presented in Fig. 1  and our review process is shown in Fig. 2. Through our review process we identified two main categories of Federated Learning applications related to cancer and oncology: whether the study was designed exclusively with cancer as its intended use-case, or whether cancer datasets were used for benchmarking a general method (Fig.
1   category is also further divided into three sub-categories: the first one contains the Federated Learning feasibility studies and methods that have been applied to the analysis of cancer datasets (i. e. , 'Framework' in Fig. 1 -Sub-Category). The second contains Federated Learning frameworks proposed or developed for 'Cancer Analysis', although almost all fail to secure relevant and novel cancer datasets and hence resort to open-access data. Finally, the third sub-category contains Federated Learning studies that address and analyze 'Privacy' of cancer data and computation.