dataesr/scientific-paragraphs-categorization huggingface dataset:
Similarly, Estrada-Peña et al. In the right panel, variables in which GBIF-mediated data was used (Used_GBIF), in the left panel variables in which data was not obtained from GBIF (NonGBIF). Next to the bars, the number of variables by each epidemiological level, and percentage in relation to the total number of variables of each group (Used_GBIF: 120 and Non-GBIF: 238). Taxon classes are grouped by taxonomic associations (e. g. , birds, primates, ticks, mosquitoes); however, some were merged to simplify the figure. For example, mamm/oth/var includes multiple mammal species which were sparsely mentioned; similarly, hosts/res var, vector other and path other grouped several species participating as hosts/reservoirs, vectors, and pathogens, respectively. Bar colors represent epidemiological levels (pathogens, vectors, hosts/reservoirs), and Other (in sienna) includes species participating as hosts' regulator, predators, among others.
GBIF-mediated data was only used in three pathogen variables (purple), representing only 2. 5% of the variables in which GBIF-mediated data was used, resulting in a remarkable difference with other sources (NonGBIF), in which pathogens represented a 59. 7%. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) Fig. 5. Data sources according to epidemiological level and scale. Representation of the data sources (left column) used for each epidemiological level (central column), and the scale of the corresponding data sources (right). Colors of the left column correspond to general data-sources categories; for example, green corresponds to biological/biodiversity data sources (e. g. , Biodiversity repositories and biological general source).
Thus, the low availability of pathogen data in repositories such as GBIF, does not necessarily hamper their contribution to disease research. However, biodiversity repositories could consider expanding their taxa coverage and scope, potentially strengthening the representation of non-free-living organisms such as pathogens. The Museums and Emerging Pathogens ECHO Program may represent a good example of an initiative that aims to integrate wildlife biorepositories with data related to emerging infectious diseases  [40]. On the other hand, taxa coverage may consider including domestic animal records. Near onethird of the pathogen records were obtained from non-human vertebrates, from which half were domestic. In this context, pathogen occurrences detected in domestic animals fall into a grey area of biodiversity repositories scope.
Furthermore, domestic animals are widely distributed and participate in several zoonotic diseases, such as cattle in Rift Valley fever  [41] , and domestic dogs in multiple helminthiases  [42] , and their contact with humans and wild animals may facilitate pathogen transmission. In this study, we excluded domestic animal occurrences, following the general scope of GBIF and other biodiversity repositories, mainly focused on natural and wild-based records  [43]. However, domestic animal occurrences in natural and unsupervised settings may be considered as relevant for ecological monitoring  [44]. In fact, GBIF offers data of domestic animals' occurrences, usually individuals under natural settings, living under freeranging conditions with minor influence of humans (e. g. ,  [45] ). Even though being a species out of GBIF scope, humans were in fact the most frequent host of pathogen records.
However, even raw data is mostly presented with different information, different terms, or taxonomic mismatches, i. e. , not following FAIR principles. Standardized data were exceptions, such as GenBank  [56] , Mammals Networked Information System (MaNIS  [57] ), Atlas of Living Australia (ALA  [58] ), and GBIF, most of them related to biodiversity data management. Standardisation of data facilitates sharing and reuse, and it has been adopted for several data types (a list of standards can be found in FAIRSharing resources,  [59] ). Scientific journals, publishers and other open data initiatives have make great advances in the open data policies  [15, [60] [61] [62] [63]. However, most of the repositories of supplementary materials and data journals do not request standardized protocols, such DRYAD  [12]  and Zenodo  [13].
The contribution of biodiversity data repositories for infectious disease research can be also strengthened by means of incorporating systems in their searching platforms that consider specific requirements of disease-ecology research. For example, beyond offering single species record, searching engines may consider interactions or associated occurrences expressed as host-pathogen, host-host, predator-host, vectorpathogen, among others, reflecting the observation of an individual host together with the pathogen occurrence  [3, 68]. These systems have been already implemented by platforms such as the Global Biotic Interactions database (GloBI;  [69] ) and ARCTOS  [70] , both of which enable the search of 'preys on' and 'host of'. In the Darwin Core standard these associations can be informed in the terms AssociatedOccurrences and a class of terms ResourceRelationship; however, they have not been used extensively  [17].
In these emerging challenges, clinical and health databases must not be on the side-lines. In addition, an improvement to the contribution of biodiversity repositories for infectious disease research may require a discussion in relation to the specific institutional scopes and taxonomic extents of the data provided. We advocate for the formal incorporation and recognition of biodiversity aggregated databases as critical infrastructure for multi-host disease understanding, predictions, and general human and animal health research, and invite researchers to share and reuse data, incorporating interdisciplinarity and cross-domain approaches in dataintensive research. The evidence of the contribution for infectious disease research justifies current and future actions to support biodiversity data mobilization, from generation, aggregation, accessibility, and final reuse.
Acknowledgements We thank  Theeraphap Chareonviriyaphap ,  Florence Fouque ,  Luna Kamau , and  Carlos Zambrana-Torrelio  for their support as members of the GBIF Task-group to Enhance GBIF Enabled Research on Species Linked to Human Diseases. We also thank  Roderic Page  and  Arturo Ariño  for stimulating discussions that inspired planning of this study.
It consists in three main types of modules: • database, object base, and knowledge-base modules, • modules that generate the initial schedules, and • user interface modules. Clearly, the user interface modules are important for implementation success. Their goal is to give to the decision maker the opportunity to modify the scheduling solution manually and to receive performance indicators for these solutions for taking the right decision. This is particularly true in the context of Industry 4. 0. Robustness is one of these indicators when dealing with uncertainty. Ref. [7]  defined a solution as robust if its performance remain relatively unchanged when exposed to uncertain conditions. This definition is typically consensual but needs to preliminary answer the two following questions: 1. How to qualify the uncertainties and how to integrate them into the scheduling problem? 2.
• The fuzzy description method is based on the possibility theory. When problem data are imprecise or not available, the uncertainty parameters are modeled in fuzzy sets and linked to satisfaction functions. The probability description is relevant when information about the behavior of uncertainty is available. The bounded form is typically relevant when this information is not enough in order to develop an accurate description of the probability distribution, but only error bounds can be obtained  [16]. The fuzzy description [17] is relevant when historical data are not readily available. Regarding the Industry 4. 0 context, it is reasonable to consider that data coming back from the shopfloor can be used to build sufficiently rich information on the uncertainties. Thus, a probability description seems particularly relevant in this context.
The literature review confirmed the used of the three types of models and the associated robustness metrics. Second, they pointed that uncertainty in scheduling is receiving more and more attention. Moreover, Ref. [26]  concluded that probability description is the most used model. Regarding the integration of new enabling technologies in scheduling practices, big data and machine learning techniques have already been considered as emerging trends. Ref. [29]  reviewed the recent progress of data-driven mathematical programming under uncertainty. Ref. [30]  proposes an application of deep reinforcement learning for lexible job shop scheduling problem. Ref. [31]  proposed an architecture for a scheduling systems based on data-driven procedures. Such types of works suggest that it will be possible in a next future to extract information directly from the shopfloor (The shopfloor data collection system).
Immediate and second-wave burn bed availability tracking could rely on direct telephone contacts with the coordination cell, as in the Belgian Association for Burn Injuries plan  [21]. It could also involve a semi-automated computerized tracking system with at least daily data collection. American burn centers validated such a system to help prepare for military conflicts or disasters  [69]. Similar software proved helpful in managing ICU bed availability during the COVID-19 crisis at the regional level in France  [70]. Special attention should then be paid to system robustness and resilience. If the deployment of a burn assessment team is necessary (see recommendation R 1. 6) to account for its impact on involved burn centers, it makes sense to have the national burn coordination cell mobilize it.
Q 1. 6 -How should burn-specific specialized triage be organized and conducted? R 1. 6 -National BMCIs preparedness plans should include: -national specialized burn assessment teams to perform specialized in-hospital triage, advisably with similar composition, qualifications, tasks and tools as the European burn assessment teams (BATs); -a process for optimal patient distribution, including efficient communication and data sharing, and definition of roles and responsibilities regarding dispatch decisions. Rationale -1. 6 -Field triage of burn casualties in BMCIs is often poorly reliable  [71]. As mentioned in recommendation R 1. 3. 1, burn-specific specialized triage is impractical onscene. Many burn casualties are initially admitted to nonspecialized hospitals. This can result from quick saturation of burn centers, from an active choice not to primarily saturate burn centers with the wrong patients, excessive distance between the disaster site and burn centers, or a combination of those factors.
- The template assessment form should be completed for each patient to gather a suitable and easily usable set of relevant clinical data (Fig. 1 ). Patient identification, including gender and age, is vital for appropriate dispatching. It should be paid the most attention. The form is divided into three frames. The surgeon completes the first one about burn wounds, potential associated injuries, and surgical management. The intensivist anesthesiologist completes the second one about general status, organized by vital functions and corresponding therapy. Both jointly complete the third one with their conclusions and triage recommendations. The completed assessment form is also a synthetic patient record to share crucial information with the transport team and the burn center at destination.
- The overall aim should be to provide all burn casualties with the same probability of recovery as in non-disaster conditions. The 2015 Formosa water park disaster in Taiwan has shown that such a goal is at hand in a well-prepared, high-level healthcare system faced with hundreds of severe burn casualties, many of them with inhalation injuries  [25, 100]. In this disaster, the remarkably high observed survival was precisely achieved with a similar strategy of initial admission of many patients in nearby nonspecialized hospitals and subsequent in-hospital re-triage by visiting burn care specialists  [23]. Whether such an outcome could be achieved in combined burn and trauma disasters is uncertain. However, except for severe hemorrhagic trauma casualties who bleed to death too early to be evacuated, US military data suggest it is indeed possible.
Acknowledgments The authors warmly thank  Dr Evelyn Depoortere  and  Ms Diana Burghardt  ( European Commission ) for their invaluable input and support along this work. This work was partly supported by funding from the  European Commission, Directorate-General for European Civil Protection and Humanitarian Aid Operations (ECHO) ,  Directorate B -Disaster Preparedness  and Prevention, Unit B1 -Civil Protection Horizontal Issues by tender contract  ECHOB1-NP-2019-05  regarding a European response to mass burn casualty disasters. references.
Additionally, below is the data for the 3 diagnostic algorithms (PFS, HGS, DS) for the median of the six readers. Fig. 4  Flowchart shows ERCPs, the gold standard, performed in patients with a potential functional stricture. Histopathologic confirmation of HD was obtained in five patients. As well, the one patient who was suspected of having FS, but had no ERCP, had FS confirmation after OLT Excluding the 4 tied cases, median PFS diagnosis was PFS in 39 cases (31%) and NFS in 86 cases (69%) for all readers, whereas our expert reader diagnosed PFS in 42 cases (33%) and 87 cases (67%). Median HGS diagnosis was no HGS in 65 cases (50%) and HGS in 64 cases (50%), whereas our expert reader diagnosed no HGS in 87 cases (67%) and HGS in 42 cases (33%). Excluding the 34 tied cases, median DS diagnosis was no DS in 53 cases (56%) and DS in 42 cases (44%) whereas our expert diagnosed no DS in 83 cases (64%) and DS in 46 cases (36%).
Bibliographic strategy The aim of this review was to repertoires all human ethanol-producing microorganisms. Only in vitro detection of ethanol was considered to de ne an ethanol-producing microorganism [EtOH-producing microbe], regardless of the atmosphere or medium used for culture. Articles reporting the presence of enzymes such as alcohol dehydrogenase in the genome which did not measure ethanol production in vitro were excluded. For this purpose, a computer program was used to search PubMed for all articles related to ethanol production for each bacterial species. For the script, we used : ""Bacteria name""[tw] OR ""Synonyms"" " consisting the name of the species, or a synonym, followed by the keywords used to detect all the articles related to ethanol production. The search was carried out without date or language restrictions.
For this purpose, we used all the species present in the NCBI taxonomy browser database [https://www. ncbi. nlm. nih. gov/Taxonomy/Browser/wwwtax. cgi], and for each species a query was used to extract the articles linked to its ethanol production. These queries yielded a total of 7,275 articles. These articles were screening initially according to their titles and abstract. A second screening was conducted by reading the full-text of the selected articles to con rm the ethanol production of each microorganism. The MS Access® database (Access 2007, Microsoft, France) was used to validate the ethanol production of all microorganisms. The output le obtained after validation is in Excel 2007 format (Microsoft), containing the taxonomic rank for each microorganism, its ethanol production (yes or no), the number of items (Query_PMID_Count) for each species and the PMID of each item.
Yeasts generally use the same pathway for ethanol synthesis, which is the "pyruvate fermentation to ethanol II" pathway according to metacyc database (https://metacyc. org/META/substring-search? type=NIL&object=ethanol+). This pathway consists in metabolizing carbohydrates until a simple sugar is obtained. The latter enters the glycolysis cycle or the Entner-Doudoroff pathway which end-product to produce puryvate. Puryvate decarboxylase further decarboxylates puryvate into acetaldehyde and CO 2. Alcohol dehydrogenase then catalyzes the reduction of acetaldehyde to ethanol (Supplementary Fig. 1 ). Human-associated ethanol producing bacteria. We found 61 bacterial species capable of producing ethanol, divided into 4 phyla, 15 families and 30 genera. The most represented were Bacillota (46 species), Pseudomonadota (9 species) at the phylum level, Lactobacillaceae (18 species), Clostridiaceae (9 species), Enterobacteriaceae (5 species), Bi dobacteriaceae (5 species) at the family level.
cerviseae  [12] , C. albicans  [50] , C. parapsilosis  [51]  and P. kudriavzevii  [48]. On the other hand, a closely related species to S. cerevisea [which is known to produce ethanol] S. boulardii have been described in the literature as probiotics for NASH patients  [52]. This repertoire will help to understand the potential involvement of ethanol-producing microorganisms in the onset of certain diseases. It can be used by clinicians in their diagnostic process following the isolation of one or more ethanol-producing microorganisms from a patient. One of the limitations of this review may be linked to the use of a single database, in this case Pubmed, but also to its temporality, as only publications submitted before January 28, 2023, were taken into account.
To promote the exploration and crossevaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed.
Similar techniques were also applied to the study of human gait  [12] ,  [13]  with the objective of detecting abnormal gait (e. g. , limping, imbalances) or extracting parameters such as average speed and stride length that have important medical significance  [14]. Furthermore, timefrequency distributions such as Short Time Fourier Transform (STFT) or Wavelet techniques were used to characterize the micro-Doppler signatures of the gaits, from which features and valuable information were then extracted. Deep learning techniques have also been proposed, whereby the feature extraction step is performed by neural networks that take the spectrograms directly as input or other radar data representations such as range-time plots or 3D radar cubes  [4] ,  [7] ,  [15]. These networks can be convolutional neural networks and convolutional auto-encoders  [16] ,  [17] , which interpret the radar data as images, or recurrent networks such as LSTM (Long Short-Term Memory)  [18] ,  [19] , which interpret the radar data as a temporal sequence of values, or architectures based on their combinations.
However, many of these algorithms were typically implemented and evaluated on relatively small, proprietary datasets. These datasets are largely different from each other with respect to activity types, input data format, radar characteristics, the geometry of the data collection, and classification algorithms, specifically both the extracted features and the selected classification algorithms. Therefore, it has been difficult to compare the performance of the proposed radar-based classification methods on a common benchmark, unlike in other research fields such as image or audio processing, where common datasets are routinely used as the benchmarks for proposed algorithms. To address the lack of a shared dataset, the University of Glasgow (UoG) released 'the Radar signatures of human activities' dataset  [20] ,  [21]  in 2019 with the descriptors in the read me file and sample code to process the database (UoG dataset for further references).
In 2020, the Radar Challenge "Human Activity Classification with Radar" was organized at the IET International Radar Conference 2020. Because of the COVID-19 global pandemic, the event was postponed to 2021 before being canceled altogether. Despite those disruptions, the challenge carried on as an online event and attracted contributions from several international institutions and researchers. In this paper, we aim to present the results of this challenge benchmarked on a common dataset. This is not a review paper but an analysis of the submissions for the international radar challenge 2020 and put them in the context of the state of the art. We will review the main methods/models and findings from the works submitted to this challenge. Statistical information on the most successful methods is also provided, and those models are explained in more detail.
Other sensing modalities can be used for activity monitoring, and other classification algorithms and neural networks could be employed too. However, the scope of this paper is not to provide a comprehensive review of all possible methods but to discuss in one place all the different proposed algorithms for the UoG dataset  [20] ,  [21]  at the challenge event. Furthermore, while the main focus of this paper is on the analysis of training methods and classification models, we also present other aspects such as pre-/post-processing steps, training hyperparameters, data augmentation, choice of loss function, and model ensemble methods.
II. THE DATASET This section provides a brief description of the dataset released for the radar challenge. The details of this dataset can be found in  [20] ,  [21]. The radar signatures were recorded using a COTS FMCW radar from Ancortek model 580-B. The centre frequency of the chirp was 5800 MHz and the signal bandwidth 400 MHz. The pulse repetition frequency was set to 1 kHz and the transmitted output power +18 dBm. The antennas at the transmitter and receiver were identical -17 dBi Yagi. The raw radar data is composed of de-chirped complex beat frequency samples for each recorded file, with 128 In-phase and Quadrature (I & Q) samples per sweep or chirp. These are the so-called 'raw' data, as no pre-processing has been applied yet to the data. Similar to common cross-evaluation approaches for other data classification challenges, we proposed using these data for   [21]  system training and two stages of evaluation: seen dataset and unseen dataset, as shown in Table  I.
The seen dataset is a subset of the training data set. The unseen dataset is a dataset that was collected from four additional subjects and that was not available to participants in the classification challenge at the stage of training machine learning algorithms. The motivation was that testing results from the unseen dataset would indicate how well the classifier or network could generalize the model as well as avoid overfitting. Furthermore, by comparing the prediction results from the evaluation of seen data vs. unseen data, we wanted to see where models could perform poorly in feature selection and classification for some of the activities. As indicated in Table  I , it is worth highlighting that the dataset contained data from 8 different indoor environments: UoG laboratory room; UoG common room; UoG MAST lab; Glasgow NG homes (3 rooms); Age UK West Cumbria (2 rooms).