dataesr/scientific-paragraphs-categorization huggingface dataset:
It is stereotyped (rtDatabase). It specifies the common characteristics of sensor data and derived data stored in the database and it has the following attributes: (i) value, representing the value of the data (ii) timestamp, showing the time at which the attribute value is updated, (iii) duration 1  , which is the time interval at which data is considered valid, (iv) type, representing the type of data (e. g. speed and position), and (v) unit of the measure. The duration attribute is defined with the (nfp) stereotype since it represents a non-functional property. Besides, the Measurement class has the saveData() operation, defined with the stereotype (sporadic) since the data are stored in the database only if the difference between the stored data value and its last updated value (i. e. , the value collected from the sensor) is greater than the maximum data error  (Amirijoo et al.
, 2006). In addition, this sub-class has the operation updateValue() stereotyped (periodic) since the data value is updated periodically by the sensor. However, the DerivedData sub-class has the operation updateValue() which is stereotyped (sporadic) because the data value is updated sporadically (when the value of one sensor data used in its calculation is updated). FusionUnit. This unit allows processing periodically the collected data from a single sensor (e. g. , reducing noise in sensor reading, processing data with sensor parameters and extracting information from images). Thereby, the processData() method is stereotyped (periodic). This unit allows also fusing data issued from multiple sensors in order to produce a consistent estimation of the controlled driving situation. In fact, the mergeData() operation is stereotyped (sporadic) as this operation is executed only if the value of one of the used sensor data is updated in the database.
Second, the FusionUnit processes periodically data (e. g. , converts signal and extracts information from images) to obtain a precise environment state. So, the ProcessData() operation is defined with the stereotype (periodic). Then, the data are stored in the database (if the difference between the stored values and the updated values exceeds the maximum data error of each data  (Amirijoo et al. , 2006) ). Therefore, the SaveData() operation is stereotyped (sporadic). Afterwards, if the sensors provide redundant or complementary data, then the FusionUnit merges the data in order to produce a consistent estimation of the controlled vehicle and an accurate description of the external vehicle environment. Therefore, the operation MergeData() is stereotyped (optional). Finally, the FusionUnit updates the value of the fused data only if the value of one of the used sensor data is updated.
Moreover, this unit fuses the redundant data provided by the two radars to estimate the accuracy state of the detected vehicle. These data are then stored in a RT database to guarantee the temporal and the logical constraints. The controller reads the stored data and calculates the desired acceleration or deceleration to maintain the safety distance. Thus, these data constitute the derived data. Moreover, Fig. 7  shows that the optional elements are instantiated since the ACC system uses sensors that provide redundant data. Therefore, the operations mergeData() and getValue() are instantiated respectively by the operations fuseData() and getValue(). Each class instantiated from the pattern is stereotyped automatically (PatternClass). For example, Radar class plays the role of an exteroceptive sensor in the Sensing pattern.
Example 2: lane departure warning system LDW system is a vehicle lateral guidance system which warns the driver when the vehicle is unintentionally drifting out of its lane  (Tass BV, 2012). This system maintains the vehicle position by detecting lane markings using a camera sensor. The data acquisition of LDW system consists of vehicle sensors and a camera. The sensors are used to determine the vehicle state (e. g. vehicle speed and steering angle), while the camera, it is used to register a monochrome view of the road in front of the vehicle. The processing algorithms extract the desired information, such as lane width and vehicle lateral deviation. These data are stored in a RT database taking into account the RT data and transactions constraints. The controller calculates the distance between the lines and the vehicle using the information collected by the camera and sensors in order to determine the appropriate action.
Conclusion and future work Designing RT database applications is a delicate task since data and transactions timing constraints must be taken into account during the design process. Therefore, it is necessary to benefit from the existing reusable design techniques, such as patterns. In order to design more comprehensible and more flexible RT design patterns, we proposed, in this paper, a UML profile for RT design patterns representation. This profile includes various extensions of UML diagrams (i. e. class diagram, use case diagram and sequence diagram). These extensions allow distinguishing between the fundamental elements and the variable elements. They also allow identifying easily pattern elements in the system model. Moreover, this profile provides extensions that specify explicitly the RT features of RT database applications.
For the evaluation of the raw, full spectral XRF data, the PyMCA software was used  [12]. As we have previously demonstrated, PyMCA allows one to estimate the concentrations and/or layer thicknesses of cultural heritage samples based on fundamental parameter calculations. We successfully applied this approach with a portable XRF system (featuring a polychromatic X-ray tube) on glass standards and an Egyptian faience  [13]. Further, we used it on multi-layer reference samples on a 16th century easel painting  [14]. Fundamental parameters were taken from the internal database of PyMCA, geometrical parameters were directly measured and instrumental parameters obtained from the instrument description and refined by the measurements of reference materials and a metallic Pb sample (flux 3. 4 × 10 7 photons/s, time 300 s, detector active area 0.
Data treatment methodology As the amount of data obtained from hyperspectral measurements is huge, statistical methods were carried out to reduce the size of the data cube in ENVI. We used the spectral angle mapper (SAM) algorithm to cluster the spectra of the data set according to their similarity to endmember spectra (Fig. 1b, c ). Endmembers are extreme data points that cannot be represented by any positive linear combination of other spectra in the data set. A first set of endmembers was derived by using the 'Spectral Hourglass Wizard' of ENVI, which performs an MNF-PCA transformation (Minimum Noise Fraction-Principal Component Analysis) on the data. The extreme points of this distribution were identified calculating the pixel purity index (PPI) for all pixels and selecting the 10,000 most extreme ones.
In a multi-dimensional visualization, endmembers were manually selected. The resulting spectral angle map is shown in Fig. 1b. This approach has been described in detail by other groups before us  [2]. This approach provided an overview of the distribution of pigments, but not all endmember spectra were representative for the pixels associated to them, allowing one to identify individual pigments or mixtures thereof. Therefore, taking these results into account, pixels representing pure pigments were selected from the data set and used as endmembers for the SAM (see Fig. 1c, d ). The SAM maps were used as a guide to select areas for investigation by XRF spot analysis. The information provided by XRF and reflectance spectra were used to identify the composition of mixtures. To investigate specific effects such as shadowing or highlighting, or to investigate details with a high degree of precision, we acquired hyperspectral images with a 40 μm spatial sampling as well as XRF maps.
In the KM theory the diffuse reflectance (R) of an infinite optical thickness layer is described by: K S = (1-R) 2 2R , and inversely: R = 1 + K S - K S 2 + 2 K S , with K the effective absorption and S effective scattering coefficients. In simple cases (taking into account diffuse illumination and collection), the K and S coefficient of paint mixtures are linear combinations of the paint components weighted by their concentrations. This description works well for pigment mixtures and transparent, superimposed layers  [28]. A perfect match of the model with the experimental data was obtained between the theory and the experimental curve for 42% of brazilwood and 58% of lapis (Fig. 5 ). For this calculation we used the spectra recorded on the manuscript, both layers are applied on highly scattering white paint.
Integration Within the Standard of Care The Medly program was implemented as part of the standard of care at the UHN Heart Function Clinic in Toronto, which serves patients with complex and advanced HF. Other services currently embedded within the clinic include in-depth teaching from clinic staff about the chronic nature of HF, necessary lifestyle changes, and how to manage complex medication schedules. Typically, relatively stable patients are seen for regular follow-up visits every 6 months, with more acute patients seen more frequently as required. It is also not uncommon for patients to consult with clinic staff over the phone or by email in between visits. The Medly program is intended to enhance these existing health services, not replace them.
The Medly Telemonitoring System and Services Central to the Medly program is an algorithm-based smartphone app, which patients use to record daily weight, blood pressure, heart rate, and symptoms as soon as they wake up. If there are signs of deterioration in a patient's health, the Medly algorithm triggers a self-care message displayed to the patient in the Medly app. In addition, an alert is sent to both a nurse practitioner (NP; via a secure Web-based clinical dashboard) and the most responsible physician (MRP) via automated emails (Figure  1 ). The MRP is the physician who has overall responsibility for directing the medical care of a patient; in the context of the Medly program, this refers to the staff cardiologist responsible for the longitudinal care of patients in the Heart Function clinic.
Clinician role The literature neither provides consistent answers regarding the optimal duration of enrollment nor the intensity of monitoring in a telemonitoring service  [8]. Understanding the degree to which these program components can be adapted while maintaining fidelity can produce cost savings through the optimization of resources and inform scaling strategies for telemonitoring programs. Duration of patient enrollment and intensity of monitoring (business hours vs 24/7) The program was launched by providing patients with a Medly kit, which includes a smartphone installed with the Medly app, a Bluetooth-enabled weight scale, and a blood pressure cuff, which allows for automatic data transfer from these devices to the Medly app. A telehealth analyst (THA) role was created within UHN's telehealth department to provide technical support by telephone, email, or in person to both patients and clinician user groups.
Interview Guide Development Separate interview guides were developed for patients and clinicians to inform possible strategies for lowering costs and improving program efficiency by gaining a better understanding of the program's soft periphery. Specifically, participants were asked to comment on the topics presented in Table  1. In their responses, participants were encouraged to consider HF telemonitoring in general, and not just the Medly program. The Medly software (with embedded rules-based algorithm) was developed around the program's theory of change  [19]. As such, it is considered part of the program's hard core; thus, no probes related to this component were included.
Recruitment Patients (n=23) were identified through purposeful sampling based on age, gender, and time since enrollment in the Medly program to ensure a variety of perspectives. This included patients who were interviewed immediately after enrollment and, thus, had no prior experience being monitored in the program. Interviews with patients were conducted until theme saturation was reached (no new themes or perspectives were found in the data)  [20]. This was achieved by setting an a priori target of 20 patient interviews. Three additional interviews were conducted, which yielded no new findings, thus confirming theme saturation. All clinicians actively monitoring patients using the Medly system at the time of the interviews (n=4) were invited to participate. In addition, 4 clinicians within the UHN Heart Function Clinic who had not yet begun using the system were also interviewed to obtain the views of nonusers.
Interview Procedures and Analysis Patients had the option of being interviewed in a private room at the UHN Heart Function Clinic during one of their regularly scheduled visits or over the phone. Clinicians were interviewed in their private offices. Interviews lasted 15 to 60 min and were recorded and transcribed verbatim. Transcripts were analyzed using conventional content analysis  [21] ; PW and KG each independently coded the transcripts and then met to discuss the results and discrepancies with themes. Once a finalized coding scheme was agreed upon, it was used to code the transcripts before a final analysis of themes. NVivo version 11 (QSR International, Doncaster, Victoria, Australia) was used to organize the data analysis.
Adapting the Telemonitoring Service On the basis of the qualitative findings, PW and KG interpreted the degree to which each of the Medly program components explored in the interviews could be adapted without impacting program fidelity. Ideas for redesign were discussed during biweekly operations meetings and prioritized for implementation according to their (1) potential to impact sustainability and scalability through cost reductions and optimization of clinic resources, (2) feasibility of implementing the change, and (3) perceived risks of negatively influencing program fidelity (and ultimately effectiveness).
Bring Your Own Device When the Medly program was launched, the intent was to shift to a BYOD model; however, at the time of the interviews, only minimal plans had been made to operationalize this change. The interviews highlight that clinicians were generally supportive of patients using their own equipment as it was necessary to ensure the sustainability of the program: I think (a BYOD model) is excellent. In fact, I've had patients ask me about it. I think, for sure that would be helpful and certainly, more cost effective because we obviously can't give kits to everybody. [Clinician 3] Some concerns were raised about the questionable quality of patients' current equipment and the fact that, in the absence of Bluetooth data transfer, patients may accidentally manually enter values incorrectly.
I may be camping somewhere where I am not accessible. But I think the whole thing of the Medly system is it doesn't rely on me [seeing] the alerts, the patients are instructed to do things [by the algorithm]. We have set up a plan and they have to act accordingly. They don't have to wait for me to respond to [follow the instructions]. [Clinician 8] Patients both with and without experience in the Medly program felt that someone should be available to respond to alerts 7 days per week but that this may also be contingent on the disease severity of the patients enrolled in the program:.
Redesign of the Medly Program for Sustainability and Scalability Qualitative results related to opportunities to modify components of the Medly program were interpreted and classified according to the degree to which they could be adapted while maintaining program fidelity. As shown in Figure  2 , the format of technical support and the peripheral equipment used were considered highly adaptable and, thus, clearly part of the Medly program's soft periphery. The participation of a clinician (role and intensity of monitoring) and the monitoring of patients over time are central components of any telemonitoring program theory of change, indicating some overlap with the intervention's hard core. However, the interviews suggest some degree of adaptability depending on contextual factors, which explains why intensity of monitoring, clinician role, and duration of enrollment were classified as moderately adaptable and part of a fuzzy boundary between the hard core and soft periphery.
Decisions related to the Medly program Opportunities for adaptation Move forward with the implementation of a hybrid Bring Your Own Device model, whereby most patients use their own mobile and peripheral devices with some Medly kits still being distributed to patients in need (eg, lack of ability to pay, low cognitive ability, and dexterity problems). This involved change to the operational procedures, including (1) generation of a list of recommended clinically valid weight scales and blood pressure cuffs for patient purchase, (2) clinician prescription of peripherals so that costs can be reimbursed by private medical insurance or tax deductions, and (3) expanding technical troubleshooting procedures to cover the most common devices on the market. Software development needed to implement this decision included (1) the development of a manual entry version of the Medly app with features to protect against inaccurate data entry and (2) adapting the Medly app for tablets.
Limitations First, although participants were asked to consider their responses with respect to telemonitoring in general, it is likely that their responses were influenced by their experiences with the Medly program. In stating this limitation, we emphasize that our intent was to describe adaptations to a specific telemonitoring program rather than to provide a detailed blueprint for implementing all HF telemonitoring interventions in any given clinical context. We argue that the context-dependent nature of implementing complex interventions makes the creation of such a blueprint impossible. Rather, we have sought to provide foundational considerations for developers of telemonitoring programs and for implementation scientists who wish to sustain and scale existing telemonitoring programs to other clinical sites and health care organizations.
Second, the pragmatic nature of this study meant that patients can be enrolled in the Medly program without consenting to participate in the evaluation activities, making them ineligible to participate in the interviews. Our inability to purposely sample these patients may have led to selection bias. Third, the interview guides were developed to probe the opinions of users on specific program components. We recognize that our approach for compartmentalizing and defining the various components of this complex intervention was subjective and context specific; this should be considered when determining the transferability of results to alternative settings. Finally, although the resulting user-guided adaptations are expected to maintain the fidelity of the intervention, the true impact of these changes was not empirically tested in this study.
Rumen Fungi The flagellated zoospores of anaerobic fungi (Neocallimastigomycetes) were first observed in the early 1900's. However, it was not until the 1970's that their true identity was confirmed  (Orpin, 1974 (Orpin, , 1977a)). To date, nine anaerobic fungal genera have been characterized with many other uncultivated taxa known to exist  (Koetschan et al. , 2014; Edwards et al. , 2017; Paul et al. , 2018). Paul et al. (2018)  attempted to get consensus on the diversity of anaerobic fungi inhabiting the guts of herbivores and concluded that among the cultured genera, Piromyces was the most represented with Buwchfawromyces being the least represented in sequence data obtained from the Genbank database. Paul et al. (2018)  also suggest that possibly another 25 new genera exist in the guts of herbivores, which remain uncharacterized.
Irrespective, metataxonomic data is highly variable due largely to the differences in techniques employed across published datasets (see Metataxonomy section) and animal variation. As such the use of gene biomarkers using metagenomics and/or metranscriptomic approaches may be more useful given that rumen microbes possess genes coding for a high level of functional redundancy  (Edwards et al. , 2008; Weimer, 2015). Recent work using metagenomics and/or metatranscriptomics has confirmed significant relationships between the abundances of key rumen microbial genes and feed efficiency  (Roehe et al. , 2016; Shabat et al. , 2016; Li and Guan, 2017)  and/or methane production  (Roehe et al. , 2016). Due to the vastness of these datasets it is difficult to compare and investigate whether studies commonly find consensus genes that would serve as good global biomarkers in their correlation studies.
Microbial gene correlations with RFI, data from  Shabat et al. (2016)  and  Li and Guan (2017)  showed some consensus as both showed that genes involved in amino acid metabolism were less abundant in feed efficient animals (Table  2 ). These data corresponds with observations that feed efficient animals excrete less urinary ammonia suggesting better rumen nitrogen use efficiency  (Bach et al. , 2005; Broderick and Reynal, 2009). Consensus of other genes across the three published datasets were not found (Table  3 ). Likewise, genes correlating to methane emissions show very little consensus amongst the five papers investigated. Nevertheless, in four datasets the methyl coenzyme reductase enzyme, which is involved in the last step of the hydrogenotrophic methane pathway (Figure  1 ), showed the most correlation to methane.
The lack of consensus across experiments, whilst perhaps being biologically correct, likely also reflects the challenges associated with comparing of datasets from different animals, variation in diet, as well as differences in sampling method, sample preparations and data interpretation. Clearly more comparative large datasets are required to develop microbiome based biomarkers for estimation of RFI and methane output. Alongside this is the need to obtain samples that are representative of the rumen microbiome in a non-invasive manner. Recently it was suggested that the oral microbiome of ruminants reflects the microbial diversity seen in the rumen  (Tapio et al. , 2016) , raising the possibility that buccal swabbing may be used as a proxy for rumen samples.
, 2017). The annealing temperatures and number of cycles used for PCR are also clearly going to bias results somewhat. Nonetheless, using a basic set of standardized protocols may not be possible, due to the differing hypotheses and the complex nature of the ecosystem (i. e. , host animal), however ensuring data accuracy by using internal standards represents one approach to ensure that comparisons among datasets are valid. Pollock et al. (2018)  attempted to describe the guidelines and consensus best practices for metataxonomic studies and concluded that bead beating is critical for DNA extraction as is the use of internal standards for metataxonomic studies amongst other recommendations. Also the construction of rumen microbiome databases to aid accurate taxonomical assignment, such as RIM-DB (for methanogens;  Seedorf et al.