dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION We developed the Imaging-AMARETTO algorithms and software tools for imaging genomics to facilitate systematic interrogation of regulatory networks derived from multiomics data within and across related patient studies for their relevance to radiography and histopathology imaging features that predict clinical outcomes. We demonstrated its utility through application to three patient studies of brain tumors, including multiomics and radiography imaging data from the TCGA GBM and LGG studies and transcriptional and histopathology imaging data from the Ivy-GAP GBM study. Our results show that Imaging-AMARETTO recapitulates known key drivers of tumor-associated microglia and macrophage mechanisms (STAT3, AHR, and CCR2) and neurodevelopmental and stemness mechanisms (OLIG2). Imaging-AMARETTO provides interpretation of the underlying molecular mechanisms in light of imaging biomarkers of clinical outcomes, and Imaging-Community-AMARETTO also uncovered novel master drivers THBS1 and MAP2 that establish relationships across these distinct mechanisms.
0000059; FDR = 0. 00018), including hyperplastic blood vessels (CThbv; P = 1. 2e-11; FDR = 5. 2e-10), perinecrotic zone (CTpnz; P = 3. 4e-9; FDR = 3. 4e-8), CTpan (P =. 000087; FDR = 0. 00032), and CTmvp (P =. 0039; FDR = 0. 029). 72 CNV, copy number variation. LGG cohort that is a member of community 2 shared across the three cohorts (TCGA glioblastoma multiforme [GBM], Ivy Glioblastoma Atlas Project GBM, and TCGA LGG). For all patient-derived samples (rows), the heat maps show driver genes' multiomics profiles (columns), including DNA methylation and RNA gene expression data (left panels); target genes' (columns) RNA gene expression levels (middle panel); and relevant biomarkers (columns), including clinical and molecular and imaging phenotypes (right panels). This module includes nine driver genes (SOX8, OLIG2, EBF4, NLGN2, SHD, RANBP17, FERMT1, LOC254559, and C11orf63) that jointly act as activator and repressor (C11orf63) drivers of the 94 target genes in this module, including OLIG2.
We anticipate that our tools for network-based fusion of multiomics, clinical, and imaging data will lead to better diagnostic and prognostic models of cancer and will open new avenues for drug discovery by integrating pharmacogenomic data into these networks, toward better therapeutics of cancer. Imaging-AMARETTO predicts OLIG2 as known driver and identifies MAP2 and THBS1 as novel drivers of neurodevelopmental and stemness mechanisms in glioblastoma multiforme (GBM). These heat maps present module 75 from The Cancer Genome Atlas (TCGA) GBM cohort that is a member of community 2 shared across the three cohorts (TCGA GBM, Ivy Glioblastoma Atlas Project GBM, and TCGA low-grade glioma). For all patientderived samples (rows), the heat maps show driver genes' multiomics profiles (columns), including DNA copy number variation (CNV), DNA methylation, and RNA genes' expression data (left panels); target genes' (columns) RNA gene expression levels (middle panel); and relevant biomarkers (columns), including clinical and molecular and imaging phenotypes (right panels).
3e-38). Association of Visually AcceSAble Rembrandt Images (VASARI) magnetic resonance imaging features with this module shows that the proportion of nonenhancing tumor (f6; P =. 013; FDR = 0. 060, with nonenhancing proportion , 33%, P =. 0014; FDR = 0. 027, and nonenhancing proportion , 5%, P =. 032; FDR = 0. 17) and speech receptive eloquent cortex (f3; P =. 046; FDR = 0. 75) are correlated with the module gene expression. 74  tumor-associated microglia and macrophage mechanisms with neurodevelopmental and stemness mechanisms in glioblastoma multiforme (GBM). These heat maps present module 98 from The Cancer Genome Atlas (TCGA) GBM cohort that is a member of community 2 shared across the three cohorts (TCGA GBM, Ivy Glioblastoma Atlas Project GBM, and TCGA low-grade glioma). For all patient-derived samples (rows), the heat maps show driver genes' multiomics profiles (columns), including DNA methylation and RNA gene expression data (left panels); target genes' (columns) RNA gene expression levels (middle panel); and relevant biomarkers (columns), including clinical and molecular and imaging phenotypes (right panels).
Source code is also provided to convert Imaging-AMARETTO and Imaging-Community-AMARETTO networks for depositing networks in the NDEx 55 network database, taking advantage of its interactive features. These reports include searchable tables within and across modules and communities, including statistics (ie, coefficients, P values, false discovery rate [FDR] values) for functional enrichment, driver validation, clinical and molecular biomarkers, and radiography and histopathology imaging features. These reports also include heat map visualizations for modules (Figs 2-6) and graph visualizations for communities (Appendix Fig A1).
Proposed Methodology This section gives an overview of the proposed methodology for designing and programming modular robot system based on functional and behavioral specifications, such as pay load or needed tools (functional) and speed or torque (behavioral). In such a routine robotic design context, the proposed framework - illustrated in Fig. 1  - starts by considering the need to implement one or several assembly operations (i. e. manufacturing bill of materials mBOM) defined by the assembly planner in the manufacturing process management (MPM) system. Such information is defined according to the assembly plan that is co-generated with the CAD model of the product and its related engineering BOM (eBOM) stored in the product data management system (PDM)  [19, 20]. Among the defined assembly plan, assembly tasks requiring robotic assistance are elicited for initiating robotics engineer activity.
Functional and Behavioural Design In the preliminary steps of the proposed framework, engineering and manufacturing data from PDM and MPM systems are used and link to an ontology (i. e. knowledge base) that describes relations and rules for robot tasks definitions. The fact of combining ontology and an inference engine ensures the autocompletion of related knowledge of an actual context from an unconstrained problem. As such, Fig. 1  shows how a robotics engineer defines robot tasks such as 'assemble with' or 'move from X to Y'. These descriptive needs are linked to CAD models, e. g. workpieces, working environment, that are also mapping to the ontology in order to infer important parameters for robot structural design. By using the pre-defined requests filled with input data from the robotics engineer and PDM and MPM systems, it is possible to infer, from the ontology, parameters such as the payload, movement range or even working rates and to compute the robot trajectory.
Case Study To illustrate the proposed methodology, a simple case study is introduced. The objective is to design a modular robot system to ensure a pneumatic linear actuator assembly. In this context, some assumptions have been made, for instance here the robot initial position is predefined, the end-effector orientation will remain constant for all operations. The product to be assembled and the related working environment are described in Fig. 2. This information is stored in the PDM system (for product information) and the MPM system (for manufacturing information), then mapping within the developed ontology materialized by the concept graph in Fig. 3. According to the tasks' definition, pre-defined SPARQL queries (query language for searching into database) are executed to generate the relevant information such as assembly sequence, position and mass of each object to be assembled in the working environment.
Asphalt mixtures are complex multiphase materials showing a viscoelastic behavior. To assess the mechanical response of these materials, a typical practice is to perform a complex modulus test. An alternative to laboratory characterization is to simulate numerically this test by means of a discrete approach. These approaches are able to reproduce the mechanical performances of asphalt mixtures, but are still time-consuming. In this paper, the complex modulus test is reproduced numerically for a viscoelastic granular material by means of 3D Non-Smooth Contact Dynamics simulations. A viscoelastic phase surrounding the rigid particles is simulated by a contact model acting between them. This contact law was implemented in the LMGC90 software, based on the Burger's model. The developed contact model handles larger time step lengths to reduce the computational time.
Contact model description In the aim to model an asphalt mixture during a complex modulus test, a viscoelastic contact model was developed and implemented in the LMGC90 software. The latter is capable of modeling a collection of rigid or deformable particles of various shapes by different algorithms  [14, 15, 16, 17, 18, 19] , (see https://git-xen. lmgc. univ-montp2. fr/ lmgc90/lmgc90_user/wikis/home). The created contact model mixes the original NSCD formulation  [20, 21, 22, 23, 24]  for rigid particles to ensure the non-interpenetration criterion with a viscoelastic model acting on distant contacts.
15. The normal and tangential components of δ k are initially set to zero. Then, at the end of each time step, these components are updated using the Eq. 26 considering the R + current values. δ + k i = R + k i dt + C k i δ - k i K k i dt + C k i (26) At each time step, the yield stress criterion is checked. If the viscoelastic interaction breaks, the frictional contact law is applied instead. For our numerical simulations, a yield stress value of 2. 7×10 6 Pa was fixed. This value is high enough to avoid fatigue damage during the test. This entire formulation allows implementing the developed viscoelastic contact model in the LMGC90 software.
Concluding remarks In this paper NSCD simulations of the complex modulus test in a 2PB configuration were performed. To model the viscoelastic phase surrounding rigid spherical particles in an asphalt mixture, a viscoelastic contact law based on the Burger's model was implemented in the LMGC90 software. These simulations   reproduce the experimental campaign, where four trapezoidal samples were subjected to five frequencies and six temperatures. The outcomes in this study show that the implemented viscoelastic contact law based on the Burger's model is able to reproduce the complex modulus properties of asphalt mixtures. For different analysis conducted in this paper, the numerical results of these simulations were in a good agreement regarding experimental data. For isotherm curves, experimental and numerical data of the norm of the complex modulus E * collapse in the same curve.
For example, in test case 1, the motor is driven to about ω m = 3000 rpm by a constant positive torque command T m = 4 Nm and then shut off. The motor torque command and the ramp up and down of the actual speed trajectory are used as input and output for identification in System Identification Toolbox of MATLAB/Simulink. Figure  4  shows that the simulated outputs using the identified parameters can be well matched with the measured outputs, which demonstrates a good accuracy of the identification of the model parameters. However, it can be seen that there are still some deviations exist even in the case of a quite small output torque. Under real speed synchronization circumstance, a large speed adjustment in a short time needs a big and time-varying motor torque command, which may result in worse response.
Simulation Results The designed speed regulation controller is first tested in a hybrid DCT powertrain model built in MATLAB/Simulink (Version: R2018a). To obtain a small initial speed slip at the beginning of gear engagement phase, the acceptable speed difference in 1 → 3 upshift and 3 → 1 downshift is less than 35 rpm according to synchronizer bench test results. In the simulations, the maximum overshoot δ is chosen to be 10% and the settling time t s corresponding to the above speed difference bandwidth is 0. 2 s. The corresponding closed-loop pole locations are p det = 0. 8081 ± 0. 2048i, then the controller parameters can be obtained according to the tuning procedure mentioned above. Table  2  lists the main parameters for the MPC controller design. Figure  6  shows that the maximum overshoot and settling time of the system are coinciding with the preset values.
Burn severity calculation Burn severity was estimated using the delta Normalized Burn Ratio (dNBR). This is a multitemporal change detection method differencing multispectral single-band indices. Landsat 8 Operational Land Imager (OLI)-derived NBR data calibrated for surface reflectance (30-m resolution) were obtained courtesy of the United States Geological Survey (USGS) Earth Resources and Science Centre (EROS). The NBR indicates which areas of a landscape were burnt, as well as the severity of the burn, using the equation NBR ¼ (NIR -SWIR)/(NIR þ SWIR) where NIR is the near-infrared band and SWIR is the short-wave infrared band  (Key and Benson 2006). The difference between the prefire (15 December 2015) and post-fire (17 February 2016) NBR scenes was calculated to determine the severity of the burn relative to the pre-existing condition of the landscape  (Key and Benson 2006).
An overview of the data input for RUSLE can be seen in Table  1. Rainfall erosivity is the erosive force of rainfall. It is defined as the intensity and amount of rainfall experienced in a particular region. Erosivity is defined as the mean annual sum of storm erosion index values (EI 30 ), which is a measure of the storm kinetic energy and the maximum 30-min rainfall intensity. Accordingly, such rainfall measurements must be recorded at temporal intervals of less than 30 min. Daily rainfall data were obtained for 23 Bureau of Meteorology (BoM) (2018) and 10 Department of Primary Industries and following the method proposed by  Lu and Yu (2002)  using the equation: b Ej ¼ a 1 þ Z cos 2pf j À $ ð Þ ½ X n d¼1 R b d when R d > R 0 ð2Þ where R d denotes the daily rainfall amount, R 0 the threshold rainfall amount to generate runoff, and n is the number of days with rainfall amount in excess of R 0 in the month, and a, b, Z and v are model parameters.
As recommended by  Lu and Yu (2002) , R 0 was set to 0 mm, therefore, parameter b ¼ 1. 49 and parameter Z ¼ 0. 29, and parameter a was calculated as follows: a ¼ 0:369 1 þ 0:098 exp 3:6c=M R ð Þ ½ ð 3Þ where M R corresponds to mean annual rainfall and C corresponds to mean summer rainfall (November to April). To account for natural climatic variation, the calculation of both EI 30 and R-factor values requires continuous (uninterrupted) daily rainfall data over many years (20 years according to  Wischmeier and Smith 1978) , which was not the case with the data obtained for some sites. Where rainfall measures were reported following accumulation over 2-5 days, rainfall was split evenly between the days. For BoM sites with missing daily rainfall data and where these sites were situated within close proximity to Department of Agriculture and Food, Western Australia (DAFWA) sites, DAFWA daily rainfall measures were used where there were missing BOM data for those years.
As the NSAMs contain organic carbon values and not organic matter (parameter (c)), each pixel with an organic carbon value within the K-factor layer was multiplied by 1. 72 as suggested by  Wischmeier et al. (1971). Parameter (e) was derived from soil subsystem data supplied by DAFWA using the attribute that describes the capacity of the soil to transmit water based on the least permeable layer in the top 50 cm of the soil profile. Permeability index values were assigned to each DAFWA permeability class based on those listed by  Landcom (2004). Each polygon was then assigned a permeability index value weighted by the proportion of each soil type present in that polygon. As the NSAMs contain information for sand and not fine sand (parameter (a)) as required in the RUSLE, parameter (a) was derived from both the NSAMs and DAFWA soil data.
For each soil type within each DAFWA layer, the soil code was matched with those listed by  Schoknecht and Pathan (2013). Where soil types were reported by  Schoknecht and Pathan (2013)  as consisting of fine sands (with or without other textures), it was assumed that all of the sand within these soil types could be classed as fine sand. A new percentage fine sand layer was generated by multiplying the percentage of fine sand soil types within each (rasterised) polygon of the DAFWA soil data by the NSAM percentage of sand for each pixel. As soil structure data (parameter (d)), as used by  Lu et al. (2003) , were not readily available, a uniform value of 3 was applied across the study area. Lu et al. (2003)  show that the assigned class is representative of major soils having medium to coarse aggregates with diameters between 2 and 10 mm.
Length and slope (LS-factor) values ranged from 0 to 23. 5. The overwhelming majority of values were low, exemplifying the generally flat terrain of the Swan Coastal Plain. The highest values (also seen as a small peak in the histogram) were seen on the escarpment itself, demarcating the western edge. The dune system at the coast appears as three faint bands parallel to the coast (Fig. 3 ). The land cover factor (C-factor) layer was derived from the DAFWA land-use dataset, with estimated values assigned to each land use based on values developed by  Panagos et al. (2015). Australian Land Use and Management (ALUM) classifications were matched with the most similar land uses listed by  Panagos et al. (2015)  based on description and desktop-based visual inspection of high-resolution RS imagery, with the middle value of the C-factor range from  Panagos et al.
Model evaluation The outputs from RUSLE were examined to determine if the predicted erosion rates were consistent with observations of post-fire erosion at the hillslopes scale. There are no data available for the Harvey catchment. However, studies of background erosion rates and fire impacts elsewhere in the region can provide some insights into the reliability of the predictions. A field survey was carried out to determine the degree with which RUSLE represents differences in erosion potential quantified from a rapid visual field assessment technique developed by  Morris et al. (2014). Fifteen sample sites were selected to cover a range of erosion potentials as predicted by RUSLE and based on accessibility (see Fig. 1c ). At each sample site, 10 locations were sampled, and classified into one of five erosion categories ranging from 0 (least erosion) to 5 (most significant erosion).
Water quality sampling Impacts of wildfire on water quality were assessed using data collected from six monitoring stations within the Department of Water and Environmental Regulation's extensive monitoring network (Department of Water and Environmental Regulation 2018). Samples were collected following standard protocols outlined in Department of  Water (2009a Water ( , 2009b)). The sites were selected because they met the criteria of having at least 5 years of continuous pre-fire water quality data and continuous post-fire data for 2016. Table  2  presents the characteristics of the six sites and their catchments, showing that four of the sites had all or part of their catchments burnt. Three of these were nested within the Harvey River Catchment, which was monitored near the outlet. Two control catchments were entirely outside the burn area and not hydrologically connected to surface waters directly impacted by the wildfire.
Concentrations of total nitrogen (TN), total organic nitrogen (TON), ammonia/ammonium-N (abbreviated to NH 3 ), nitrate and nitrite (NO x ), total phosphorus (TP), and soluble reactive phosphorus (SRP), together with physicochemical parameters of temperature, dissolved oxygen, pH and TSSs were analysed at all of these stations (which were always sampled at the same time of day). These pre-and post-fire data were analysed by comparing means for each parameter at each site using Welch's t-test. A principal component analysis for each site was performed to determine whether together all the measured parameters were indicating a pre-fire to post-fire shift in multivariate space. Following this, each parameter was analysed separately using multiple linear models, in which each measurement was standardised and its deviation from the site averaged, and plotted over time.
01-0. 1 to 1-10 t ha À1 year À1. Data from the erosion field survey were summarised for each site by the median erosion category among the sampled locations. These most typical erosion conditions of the site could then be related to the corresponding RUSLE prediction to evaluate model performance in terms of providing an indication of erosion susceptibility (Fig. 6 ). The results show an exponential relationship between erosion categories (from  Morris et al. 2014)  and the RUSLE predictions. Erosion categories ranging from 0 to 2. 5 correspond with erosion rates ranging from ,0. 05 to 1 t ha À1 year À1. The exponential relationship suggests that erosion magnitude scales non-linearly with the erosion categories from  Morris et al. (2014). In fact, for the equation relating the RUSLE prediction and erosion category (E cat ) (RUSLE ¼ 0:023e 2:26E cat ) (Fig.
Acknowledgements The contributions of  Jane Townsend  and the  Harvey River Restoration Taskforce in Waroona , and  Steve Fisher ,  Science Advisor  from the  Peel Harvey Catchment Council (PHCC) , were central to this work, providing the community impetus and stakeholder guidance following the severe fires in their catchments. Dr Tim Storer  provided advice on experimental design and coordinated support from the  Department of Water and Environmental Regulation (DWER). Rebecca Firth  (DWER) assisted with statistical analysis. Thanks to the  Peel Measurement Team, DWER , for the collection of long-term water quality data used in this manuscript and specifically to  Judith Le Gresley  and  Steve Keene  for assistance with the erosion fieldwork. The work was funded by  Edith Cowan University  with contributions from  DWER , PHCC and  Australian Research Council ARC-Linkage  Project  LP150200654.
Results Tomato mobilome composition. The tomato reference genome (Solanum lycopersocum cv. Heinz 1706, release SL2. 5) contains 665,122 annotated TE sequences belonging to 818 families  23. The vast majority of these annotations correspond to ancestral TE copies that have degenerated to different degrees and potentially lost their ability to transpose  24. To investigate the composition of the tomato mobilome, i. e. , the set of TE families with recent mobilization activity, we analyzed short-read whole-genome resequencing data available for 602 tomato accessions  2, 25, 26. This dataset contains wild tomato relatives (Wild, Fig. 1a ) and spans the Lycopersicon clade, which regroups wild tomatoes (S. pimpinellifolium, SP), early domesticated tomatoes (S. lycopersicum cerasiforme, SLC), and cultivated tomatoes (S.
Methods Detection of TIPs. Illumina resequencing data from 602 tomato accessions were downloaded from EBI-ENA and aligned to the tomato genome reference (version SL2. 5) using Bowtie2 v. 2. 3. 2 (arguments -mp 13 -rdg 8,5 -rfg 8,5 -very-sensitive), and PCR duplicates were removed using Picard. The detection of TIPs was performed using an improved version of SPLITREADER  58. Our SPLITREADER (vbeta2. 5) pipeline uses the information of both split-and discordant reads to call non-reference insertions. In addition, we genotyped the absence of non-reference TE insertions by analyzing local coverage around the insertion sites. SPLI-TREADER has four steps: (i) extraction of reads mapping discordantly or not at all to the reference tomato genome, (ii) mapping to a collection of reference TE sequences and selection of the reads aligning partially or discordantly, (iii) remapping selected reads to the reference genome sequence, and (iv) identification of a cluster of split-and/or discordant reads indicating the presence of a nonreference TE insertion.