dataesr/scientific-paragraphs-categorization huggingface dataset:
Electronic toolkit As a turnkey application, AS-ISK is designed as a stand-alone Excel ® -based toolkit that is independent from the source data. Compared to the previous WRA and FISK semi-automated workbooks, a turnkey application provides two major benefits: 1) for the end user, it allows the assessor to work seamlessly on database spreadsheets either located on the local computer or accessible from a network; and 2) for the developer, it facilitates provision of feedback and support by software updates that will simply replace (overwrite) previous releases. Because nearly 95% of the previous code from FISK was re-written during development of AS-ISK for the purposes of macro-and micro-optimisation (i. e. increased overall and routine-specific computational speed and efficiency, respectively), and a major re-design was applied to the GUI, limited comparability remains between AS-ISK and FISK (v2) except for the overall question-wise and scoring structure of the toolkit.
A component-wise (dialogbased) overview of the full range of features and capabilities of AS-ISK is provided in the Supplementary Information document. This includes the following sub-sections: Pre-loading -A check is performed for Excel ® version compatibility. Start -AS-ISK requires a spreadsheet, with the options of opening either an Existing or a New assessment worksheet or by Import of a previous assessment worksheet created by one of the FISK family of toolkits for re-assessment under AS-ISK (Figure  S2 ). The user can also select from five language options (i. e. English, French, Italian, Spanish, Turkish) with which to carry out the assessment Main Assessment Workspace -The core user interface where all assessment-related data manipulations can be performed (Figure  S1 ). New/Edit -The user provides all details of the taxon being assessed, either by creating a new assessment (Figure  S3a ), editing an extant assessment (Figure  S3b ), or batch editing multiple assessments (Figure  S3c ).
e. potential socio-economic impacts and those on ecosystem services; and 4) a new post-assessment evaluation of how predicted climatic conditions might affect the risks of the organism's introduction, establishment, dispersal and impact. These improvements were complemented by a change in program architecture to a turnkey application, which effectively functions like most commercial software packages in that the user interface and data are separated from the underlying program code. Therefore, rather than having the assessment data embedded within the screening tool (as do the more primitive WRA, FISK and sister tools), AS-ISK creates or imports datasets dynamically for the creation/addition/editing/deletion of new assessments and upon closure exports the resulting assessment dataset as a separate data file in Excel ® format, which is retrievable and usable independent of AS-ISK.
Acknowledgements This work was funded initially and in large part by the  UK Department of Environment, Food  and  Rural Affairs  and a  Cefas   Seedcorn grant  ( DP372 ). Further contributions by LV were through a  2221 Fellowship Programme grant  from the  Scientific and Technological Research Council of Turkey (TÜBİTAK)  and the  Department of Science Fellowships and Grant Programmes (BİDEB). We thank  E. Tricarico  for providing assisting with the Italian language component of AS-ISK, and anonymous reviewers for comments to an earlier version of the manuscript.
Methods A prospective observational multicenter international cohort design was used. The international database was declared to the CNIL (Commission Nationale de l'Informatique et des Libertés). The French ethics committee waived the need for informed consent for French centers. Similar authorization was obtained from countries such as Portugal (Centro Hospitalar S. João), Poland (Poznan University of Medical Sciences) and Australia (Royal Brisbane and Women's Hospital) and it was waived in the other countries due to the observational nature of the study.
Study protocol and definitions Patients were enrolled if they had a new diagnosis of HA-BSI and were admitted to an ICU. The study focused on the first episode of HA-BSI, either being ICU-acquired or acquired before admission to ICU. The detailed protocol has been described previously  [7]. Data collected for each patient included the dates and times of collection and of positivity of the first positive blood culture; source of infection; presence of sepsis; severity of illness; comorbidities; and infection management, including source control, antimicrobial drugs and adjunctive treatments. Organ dysfunction and organ failure were defined as Sequential Organ Failure Assessment (SOFA) scores >0 and ≥3, respectively. All study data were obtained from patient files, and no additional tests were performed for the purpose of the study.
The variables were organized into three tiers: country, ICU and patient. To identify factors associated with day-28 mortality, we built a three-tiered hierarchical logistic mixed model using the GLIMMIX procedure in the SAS software. The influence of country-based and ICU-based variables on the outcome was included through both fixed and random effects. Multilevel modeling takes into account the hierarchical structure of the data, which may manifest as intra-class correlations. To obtain a conservative estimate of the standard error, a separate random-error term should be specified for each level of the analysis. Therefore, to avoid overestimating the significance of risk factors for death by day 28, we took intra-class correlation into account, and we specified a separate random-error term for each tier.
Architecture The software architecture of "Domus" is based on the open source home automation software openHAB 5. It allows monitoring and controlling all the appliances in the flat with a single system that can integrate the various protocols used. It is based on an OSGI framework 6  that contains a set of bundles that can be started, stopped or updated while the system is running without stopping the other components. The system contains a repository of items of different types (switch, number, color, string) that stores the description of the item and its current state (e. g. , ON or OFF for a switch). There are also virtual items that exist only in the system or that serve as an abstract representation of an existing appliance function. The item is bound to a specific OSGI bundle binding that implements the protocol of the appliance, allowing the system to synchronize the virtual item state with the physical appliance state.
Future Work To truly validate the long-term acceptability of BCIs for smart home control we need a longer-term study with a cross-blind protocol. Using, as  Barachant et al. (2012)  suggest, a signal database to initialize the minimum distance classifier would lead to a better performance and make the use for end-users much more pleasant, this would also effectively remove the need to train the BCI. Although, some instructions to the user and a short calibration would be required, overall the time spent and tediousness on the part of the user would be significantly lower (comparable to operating an eye-tracker). Finally, here we preform experiments where the BCI is used for direct control, which is already possible with better and more robust modalities such as speech or gesture recognition.
Background: The Checklist for Early Recognition and Treatment of Acute Illness (CERTAIN) is an international collaborative project with the overall objective of standardizing the approach to the evaluation and treatment of critically ill patients world-wide, in accordance with best-practice principles. One of CERTAIN's key features is clinical decision support providing point-of-care information about common acute illness syndromes, procedures, and medications in an index card format. Methods: This paper describes 1) the process of developing and validating the content for point-of-care decision support, and 2) the content management system that facilitates frequent peer-review and allows rapid updates of content across different platforms (CERTAIN software, mobile apps, pdf-booklet) and different languages.
Results: Content was created based on survey results of acute care providers and validated using an open peer-review process. Over a 3 year period, CERTAIN content expanded to include 67 syndrome cards, 30 procedure cards, and 117 medication cards. 127 (59 %) cards have been peer-reviewed so far. Initially MS Word® and Dropbox® were used to create, store, and share content for peer-review. Recently Google Docs® was used to make the peer-review process more efficient. However, neither of these approaches met our security requirements nor has the capacity to instantly update the different CERTAIN platforms. Conclusion: Although we were able to successfully develop and validate a large inventory of clinical decision support cards in a short period of time, commercially available software solutions for content management are suboptimal.
Similar to surgical checklists, CERTAIN may be particularly beneficial in low-resource settings with a scarcity of formally trained personnel  [6]. During the evaluation of acutely decompensating patients, CERTAIN guides health-care providers through a structured approach, starting with a primary survey (ABCDE) followed by a secondary patient survey consisting of reason for admission, past medical history and the patient's problem list. The latter is the gateway to the clinical decision support embedded into CERTAIN. In CERTAIN software, selection of a syndrome on the problem list leads to on-demand display of point-of-care key information in an index card format in the center of the screen with recommendations regarding further diagnostic and therapeutic steps (Fig. 1 ). Similarly, point-ofcare key information is readily available or "just one click away" for selected procedures and medications.
Creation Specific cards are drafted by content matter experts utilizing primary literature and published guidelines identified via multiple databases (e. g. Medline, EMBASE, National Guideline Clearing House, Cochrane Library, Up-To-Date). Both authors and reviewers are selected from the "expert panel", a convenience sample of (currently) 81 physicians and 6 pharmacists from various backgrounds (e. g. critical care, anesthesia, emergency medicine), including many physicians involved in an ongoing study implementing CERTAIN into clinical practice  [5].
Publication and updates After a card is proofed and finalized, it serves as the blueprint to update the different CERTAIN platforms. To ensure that the content stays up-to-date, authors receive a request after one year to update their card with regards to changes in current evidence and guidelines, followed by the same review process as when creating a new card. In addition, this updating process can be triggered at any time by any CERTAIN user via an embedded feedback button in the software. While the main work flow for development and validation is based on the English content, cards are currently being translated simultaneously to other languages including Spanish, Chinese, Turkish, Croatian, Serbian, and Polish. We currently have 60 Spanish, 176 Chinese,152 Turkish,20 Serbo-Croatian and 65 Polish cards.
Ownership The coordinating center overseeing content management across all platforms is the Mayo Clinic, Rochester. This role includes development, review, updates, and translation of content and infrastructure and involves system organization, reminding authors and reviewers about update deadlines, recruiting authors, reviewers and translators for new content development and working with programmers to find technical solutions for data management issues. Individual card "ownership" and authorship is shared between the author and reviewers and acknowledged on all CERTAIN output (software, mobile app, PDF). If any original authors/reviewers cannot maintain "ownership" of a card the coordinating center assigns new individuals to adopt that card.
Software infrastructure Cards were initially created, stored and modified in Microsoft Word® using Dropbox® links for sharing. Using those links, reviewers could download the Word® files, revise and add comments to the documents and then send them back to the content lead editor as an email attachment. Unfortunately Dropbox® did not support real time collaboration between different authors simultaneously. While technologically simple, this method became impractical due to the large amount of time needed to organize the collaboration process to enable reviewers to see each others' comments and hence facilitate a virtual discussion. The potential for missed emails and slow file updates, and the lack of version control became unworkable for the content lead editor. We improved this process by switching to Google Docs®, which allows sharing of text documents via links and editing of the source files directly by multiple reviewers simultaneously.
Of note, with regards to our content management system we are not concerned about any patient confidentiality issues, because while personal data can be entered into CERTAIN software (to facilitate charting and debriefing) the cards themselves do not contain any patient information. Furthermore both Google docs® and Dropbox® required excessive work to execute simple tasks, including manually updating the software and mobile applications whenever content changed; sending email reminders to reviewers to ensure compliance with peer review deadlines; and keeping track of the cards' expiration dates. Due to the absence of readily available software meeting our requirements, we have thus started developing a customized software solution that will allow authors and reviewers to co-create, edit and review content directly in a secure cloud server with the capability to automatically update the different platforms including the PDF booklet (see Table  2  and Additional file 7: Figure  S3 ).
g. patients or local health workers) to send medical information including images and voice messages to remote specialists who in turn could provide live decision support. While we similarly encountered software issues described above, CERTAIN alleviates the acceptance issue by providing on-demand, interactive best practice advise to the onsite providers, empowering them to use, ignore or modify the information at their own discretion. Additionally, and similar to the project by Celi et al. , CERTAIN tries to address the challenge of getting technologic support to areas with potentially minimal infrastructure and/or internet capability by offering decision support via various platforms including a paper version as well as a mobile phone app. Another major challenge at the intersection between technology and healthcare that we encountered with CERTAIN itself is the difficulty of keeping a balance between doing justice to the complex environment that it is being designed for (i.
e. evaluation of critically ill patients) while keeping the interface simple since in these situations time is generally of the essence. For example, during a recent simulation study participants largely provided positive feedback about CERTAIN in general, but felt that the software should become somewhat more intuitive. It is reassuring though, that in this simulation study CERTAIN improved health-care providers' performance  [20]. It is unclear, however, whether the benefit is due to its embedded decision support or the other components such as teaching a structured approach to patient care, safety culture, and closed loop communication strategies. A before-after quality improvement study is underway to assess the impact of CERTAIN on care processes and patient outcomes when implemented into clinical practice in multiple Intensive Care Units (ICUs) across five continents after training local personnel remotely via live video stream  [5, 9].
Acknowledgements We would like to thank the many people who contributed to CERTAIN over the years. In particular we would like to thank  Lei Fan  for the fantastic work on developing and maintaining the CERTAIN software. Below is a list of authors and reviewers who have been involved in CERTAIN since its inception. Thank you to all who contributed their time and talent over the years and are still currently doing so. Thank you also to  Midhat Mujic  for his hard work with content uploading and support.
The single-nucleotide polymorphism (SNP) genotyping of the mapping population was based on restriction-associated DNA sequencing (RADseq) using Illumina short reads. We aligned the short-read data available for each progeny of the mapping population to the contigs of the 3D7 genome assembly for SNP calling. After filtering for quality and genotyping rate, we retained 245 progeny and 46,784 SNPs. We generated a linkage map comprising all SNPs called on the 3D7 contigs and obtained 17 linkage groups corresponding to the expected number of chromosomes  (31). We found no evidence for misassembly, as all contigs were uniquely assigned to a single linkage group and the genetic and physical marker orders were highly correlated (see Fig. S1C  in the supplemental material). Thirteen linkage groups were composed of a single contig, and four linkage groups were composed of 2 to 4 contigs each.
1C ). We detected large clusters of transposable elements at homologous positions in the inverted sequence. The inversion was located 142. 7 kb from the mating type alpha-1 HMG box gene. Core and orphan gene content. In order to determine whether chromosomal structural variation resulted in functional variation between the genomes, we predicted genes de novo in the 3D7 genome. We combined two types of evidence to guide the predictions, as follows: (i) we mapped protein sequences of isolate IPO323  (33)  to the genome of 3D7, and (ii) we identified transcript locations using two deep-RNA-sequencing datasets generated for the complete infection cycle of 3D7  (41). We identified a total of 11,737 genes in 3D7. the IPO323 genome were in a similar range (10,688 to 11,839)  (32, 33, 42). A total of 7,353 predicted proteins (62.
6%) contained at least one domain matching an entry in the Pfam database, and 1,034 proteins (8. 8%) were likely to be secreted extracellularly (see Table  S3  in the supplemental material). Plant pathogens secrete a large number of SSPs to modulate the host immune response. Hence, we used the machine-learning algorithm implemented in EffectorP  (43)  to identify genes most likely to encode effectors interacting with the host immune system. A screen of the secretome identified 330 proteins (2. 8%) as strong candidates for SSPs interacting with the host (see Table  S3 ). The proportions of both predicted secreted proteins and putative effector proteins were significantly larger in 3D7 than in IPO323 (Fisher exact test, P ϭ 0. 00494 and P ϭ 0. 0311, respectively). Coevolution of plant pathogens with their hosts may lead to rapid gains or losses of pathogen genes if the encoded proteins either confer a strong benefit or the host immune systems evolve to detect them.
5E  and F ). Then, we used EffectorP to identify the proteins most likely to be secreted effectors among all secreted proteins. We found that orphan genes were significantly enriched in genes predicted to encode effector proteins (Fig. 5G ). Finally, we analyzed previously generated transcriptomics data over the entire time course of a wheat infection  (41)  to determine gene expression levels and identify differential regulation. The core secretome contained a large number of genes that were upregulated during infection (see Fig. S2  in the supplemental material). Proteins predicted to be effectors were shorter overall and contained more cysteines, consistent with current knowledge regarding the characteristics of fungal effectors  (44)  (see Fig. S2  and Table  S6 ). The orphan secretome was substantially smaller than the core secretome, as discussed above (see Fig.
A PacBio SMRTbell library was prepared using 15 g of high-molecular-weight DNA. The library was size selected with an 8-kb cutoff on a BluePippin system (Sage Science). After selection, the average fragment length was 15 kb. Sequencing was performed on two SMRT cells using P4/C2 chemistry and three SMRT cells using P6/C4 chemistry. PacBio sequencing was run on a PacBio RS II instrument at the Functional Genomics Center, Zurich, Switzerland. Genome assembly using self-corrected PacBio long reads. PacBio read assembly was performed using HGAP version 3. 0 included in the SMRTanalysis suite (version 2. 3. 0, patch 3)  (40). HGAP was run using default settings except for the minimum seed read length to initiate the self-correction. The filtered SMRT sequencing data amounted to approximately 90-fold coverage of the expected genome size of 39 Mb  (32).
Assembly validation using a high-density genetic map. We used high-density genetic maps to validate the contiguity of HGAP-assembled contigs and to finalize the assembly of contigs into chromosomes. For this, we used the previously generated restriction-associated DNA sequencing data of a progeny population produced from a cross between isolates 3D7 and 3D1 (isolated from the same wheat field)  (37). We aligned qualityfiltered Illumina reads obtained from each progeny in the cross to HGAP-assembled contigs using Bowtie2  (60). SNP calling was performed, first individually per progeny using HaplotypeCaller implemented in the Genome Analysis Toolkit version 3. 3  (61) , using settings for a haploid genome and setting the maximum number of alternative alleles to 1. Then, SNP calls were combined and evaluated for the entire progeny population using the GenotypeGVCFs tool.
We filtered SNPs by failure to meet the following criteria: QUAL Ͼ5,000, QD Ͼ5, MQ Ͼ20, and ReadPosRank-Sum, MQRankSum, and BaseQRankSum between Ϫ2 and 2. The filter criteria were selected primarily to remove SNPs called from ambiguously mapped reads. High-quality SNPs and progeny were filtered for genotyping rates. First, we removed all progeny with less than 75% of SNPs being called, and second, we removed all SNPs with a genotyping rate below 90% among the retained progeny. A genetic map was constructed using MSTmap implemented in the R package  (62, 63). We chose the Kosambi distance function and a P value cutoff of 1eϪ6. Genetic maps linking individual contigs into chromosomes were visually inspected for continuous gradients of genetic distances and physical distances, evidence for contig misassemblies (a contig associated with more than one linkage group), and contig orientation within the linkage group (based on the genetic marker order).
A final polishing step was performed using PILON  (65)  and Illumina short read data generated for 3D7 in a previous study accessible from the NCBI Short Read Archive under acces-  sion number SRS383147  (31). PILON was set to correct indels and SNPs detected by aligning Illumina reads to the assembled chromosomal sequences. Repetitive elements annotation. We annotated repetitive elements in the genome of 3D7 using RepeatModeler version 1. 0. 8 (A. F. A. Smit and R. Hubley, RepeatModeler Open-1. 0 2008-2015; http://www. repeatmasker. org). RepeatModeler includes both RECON and RepeatScout for the de novo prediction and modeling of repeat families using rmblast. De novo repeat families and repeat families known from the RepBase database were classified and annotated. Finally, the genome of 3D7 was masked using RepeatMasker version 4.