dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , reducing the energy consumption while achieving large coverage and robust connectivity, so Multi-Objective Genetic Algorithms (MOGA) are adopted in  [29] [30] [31] , aiming to find a deployed network arrangement to maximize area coverage, minimize energy cost (maximize lifetime), and maintain good connectivity. The remainder of the paper is organized as follows. Section 3 presents the motivation of proposing the optimization framework. Section 4 illustrates the detailed design method and implementation of the proposed framework including the description of the architecture, related performance metrics, weighted sum cost function based optimization, multi-scenario/multi-objective based optimization, the settings of the framework via command lines, and a MATLAB based GUI as well as the generic use of the framework.
Architecture of the Framework (iMASKO) In this section, a generic GA-based optimization framework called iMASKO (iNL@MATLAB GA-based Sensor NetworK Optimizer) is proposed. It is able to quickly and efficiently explore simulation-based results or the theoretical model-based results. Note that in this context, iMASKO is applied to automatically fine-tune the results from our house-built SystemC-based simulation  [32] [33] [34]. In practical use, the optimization of related parameters in the design space can help find the application-specific solution to achieve the most optimal network performance for the given scenario before real deployment. By using iMASKO, only two things are needed: (i) a set of input parameters in the design space, and (ii) a user defined fitness function. As long as the system/model under evaluation can return the required data metrics to the fitness function, the detailed implementation of the system/model (simulators/emulators/mathematical models) can be ignored, which is of special interest for non-experts in wireless sensor network design.
Finally, the configuration of the GA optimization process is very simple in iMASKO for users, since the interfaces of the corresponding algorithm parameters are provided and the configurations can be done via command lines and a MATLAB-based GUI. Firstly, a brief workflow of iMASKO is presented in Figure  2  and the detailed optimization process of iMASKO is given in Table  2. Before starting the optimization, relevant parameters are fed to the simulation environment and to the GA evaluation engine respectively. Once the corresponding metrics have been generated from simulations and processed within the fitness function, the GA evaluation engine will decide whether the final result can satisfy the criteria of the given task. If so, the solution can be considered and applied in real applications.
Therefore, evaluations can be made in detail with PktCAFs, PktCFs, and PktOverflw in addition to PktLossPro and PDR.  Network Delay: packet latency is usually used to evaluate network delay. Packet latency can be divided into three sub-types: successful packet latency (SucPktLatency), all packet latency (AllPktLatency), and data packet latency (DataPktLatency). SucPktLatency is defined as the time interval between the instant at which the data packet is sensed and the instant at which is successfully transmitted by receiving its ACK frame. Compared to SucPktLatency, AllPktLatency takes both successful packet transmission and failure packet transmission (caused by PktCAFs or PktCFs) into consideration. DataPktLatency is calculated as the time interval from sensing the data packet to the receipt of this packet by the sink node (or coordinator).
As an example: >. /imasko -save GAresults. log. As the requirements of different tasks are application-specific and vary all the time, the complete configuration of iMASKO options via command lines not only satisfies the varying requirements, but also provides a flexible and effective way to set related parameters in the optimization/evaluation process for the given scenario. Since iMASKO command line options are not intuitive especially for non-experience designers and users, a MATLAB based GUI has been developed to link simulation and optimization, which facilitates configurations on both sides and makes the evaluation process visualizable. The GUI is shown in Figure  5 , where all parameters mentioned in the previous section can be set in the corresponding edit box. Default values are used if the parameters are not set.
Generic Use of iMASKO Except for the multiple and detailed configuration interfaces support, iMASKO is generic in its use of the optimization/evaluation process. In this work, the fitness function uses iWEEP's SystemC-based simulation results. However, the fitness function in iMASKO can be of multiple types as long as it provides parameter space inputs and performance metrics outputs. Thus, results from other well-known WSN simulators, such as NS-2, OMNeT++  [42]  and Prowler  [43] , can also be used under the evaluation of iMASKO, even if the detailed implementation and knowledge of such simulations are unknown. For instance, an executable C++ file of NS-2 and OMNeT++, as well as MATLAB's m-file of Prowler, are all able to act as the fitness function, since the required performance metrics in the fitness function can be generated after the execution of these files.
The weighted sum cost function is derived from (Equation (  4 )) and given as follows: () In this test, the same importance is given to the three metrics with each weight vector as 1. Percentage numbers are used for the performance metrics to reduce the impact of the metrics' contribution in the weighted sum. Here, TotalEnergy is given as 250 mJ (in the experiment, the maximum energy will not exceed 250 mJ). The use of twice the sample interval is because latency could sometimes exceed 100 ms (1*Sample Interval) when longer backoff wait time happens, which can be caused by the use of larger BE value. The GA implemented in MATLAB R2012a has been integrated in the iMASKO framework and used for the parameter tuning in the experiment. The parameters of the unslotted algorithm are all integers rather than real numbers.
Although MATLAB R2012a has a built-in integer number based optimization support, it provides limited configuration options. Namely, if integer optimization is applied, many commonly used options cannot be configured, such as the settings of CreationFcn, SelectionFcn, CrossoverFcn, MutationFcn, EliteCount, and CrossoverFraction are all unavailable in integer problems according to the user guide. Therefore, the default real number optimization is used in our experiments but with custom functions like CreationFcn and MutationFcn to produce integer individual parents and integer mutation children, in which the evaluation can proceed totally based on integer optimization while at the same time all the options in GA can be configured according to the problems at designers' hands. In addition, the typical data sensing start point of each node is randomly generated by feeding with different seed values, but it could also be fixed with the same sensing start time.
In addition, the computational feasibility of iMASKO for the exhaustive search of large scale scenario can be different when simulation model (model from simulator/emulator) and mathematic formula model are respectively employed. To optimize the performance metrics from simulation model, iMASKO firstly has to wait the generated metrics from the simulation run and then can proceed to the evolution process. Thus, high efficiency cannot be guaranteed when longer time is required by the simulation for the given task. However, if mathematic formula based models implemented in m-file are used, iMASKO can launch many calculations with different parameter configurations in parallel, which can greatly accelerate the whole optimization process. In summary, iMASKO is computational feasible for the exhaustive search in large scale scenario but its performance is significantly affected by the type of software model it uses (simulation/emulation model, mathematic formula model).
The multi-objective genetic algorithm in MATLAB R2012a optimization toolbox (ver. 6. 2) was integrated into the iMASKO framework and was used to generate the Pareto front for energy consumption and packet loss probability. The was configured for 60 initial integer individuals, 30 generations, scattered crossover, 0. 8 crossover fraction as well as 2 elite children. Figure  8  shows the obtained four-dimensional Pareto front from the Telos mote based network. In the Figure, the node energy consumption and network packet loss rate of the glucose sensor node are compared with the corresponding performance metrics of the respiration and temperature nodes respectively. According to the problem requirements, a good tradeoff solution for both energy and reliability can be manually selected by the decision maker based on their knowledge and intuitive experience (e.
A few base-paired nucleotides located at the ends of a helix or near a bulge or a loop also showed  reactivity (e. g. , nt 22, 25, and 26). Despite such a high conformity between the predicted and the SHAPE-validated structure models, some noticeable differences could be observed. First, SHAPE data indicated that the actual apical loop of SL2 is larger than predicted by Mfold (Fig. 1A, B ). Second, this loop is shifted by 2 nt, inducing different base-pairing in the upper part of SL2, including the primer binding site (PBS). Third, a small SL was observed in the SHAPE-validated structure at the base of Gag SL2, which was not present in the predicted structure model of the MPMV packaging signal RNA. Finally, even though the same pal SL is present in the two structures, it is interesting to observe that the nucleotides in the loop, which contains the "GC" dyad, displayed weak or no reactivity, suggesting that these nucleotides are indeed base-paired.
MATERIALS AND METHODS MPMV RNA secondary structure analyses in silico MPMV RNA (388 nt and 550 nt) secondary structure was predicted using the Mfold server  (Mathews et al. 1999; Zuker 2003 ) and validated by applying SHAPE reactivity data in the RNAstructure software (version 5. 3)  (Reuter and Mathews 2010). The predicted RNA secondary structures were redrawn with XRNA software. Sequences from different strains of MPMV corresponding to packaging signal RNA were employed to predict the RNA secondary structures of the region using Mfold  (Mathews et al. 1999; Zuker 2003). A sequence alignment of the same region for these MPMV isolates was also generated using ClustalW to look for the conservation of sequences in different structural motifs. MPMV nucleotide positions refer to GenBank accession no.
2008). The data were then further normalized for outliers by determining 10% of the highest values and excluding the highest 2% of these values. The remaining 8% of the values were averaged, and the average value was used to divide the SHAPE data. The SHAPE reactivity data (provided as supplemental data) obtained for each nucleotide were then applied as constraints in the structure prediction program RNAstructure (version 5. 3)  (Reuter and Mathews 2010 ) in order to obtain the SHAPE-derived RNA structure of the MPMV packaging signal RNA.
In vitro dimerization assay In vitro dimerization was performed according to the method described previously  (Marquet et al. 1991). Briefly, 300 nM of the purified wild-type or pal mutant RNAs was incubated in dimer (250 mM sodium cacodylate at pH 7. 5; 1. 5 M KCl; 25 mM MgCl 2 ) or monomer (250 mM sodium cacodylate at pH 7. 5; 200 mM KCl; 0. 5 mM MgCl 2 ) buffer for 30 min at 37°C. Following this incubation, loading dye containing glycerol was added, and samples were subjected to electrophoresis in native 1% TBM (50 mM Tris base, 45 mM boric acid, 0. 1 mM MgCl 2 ) or semi-denaturing TB (50 mM Tris base, 45 mM boric acid) Agarose gel at 4°C or 20°C, respectively, stained with ethidium bromide and visualized for band shift using ultraviolet (UV) transillumination. Band intensities were measured using Quantity One software, and percentage (%) of dimerization was calculated for each mutant employing the formula: (Intensity of dimer band -Intensity of background)/[(Intensity of monomer band -Intensity of background) + (Intensity of dimer band -Intensity of background)].
ACKNOWLEDGMENTS We thank  Reka Raghunathan Chakravarthy  for her assistance in cloning some of the mutant clones. This research was funded primarily by a grant from the  United Arab Emirates University  ( UAEU-NRF 26 ) to T. A. R. and, in part, by a grant from the  College of Medicine and Health Sciences  ( NP 09/18 ). R. M. is supported by the  Centre National de la Recherche Scientifique  (C. N. R. S. ). S. J. A. is supported through a scholarship from the  Office of the Vice Provost for Research and Graduate Studies of the UAEU. Received June 26, 2013; accepted September 11, 2013.
A laboratory experiment to gather biological data on an accession of P. psidii s. l. from Australia was also performed to add to existing published data on other accessions from Brazil  [33, 34]. The distribution data, except for Australia, China and Japan, was then combined with all empirical biological information available into the CLIMEX modelling software to develop an improved climatic niche model and generate more robust estimates of the rust's potential distribution worldwide. The fit of the model was tested using the Australian distribution data and the point records in China and Japan. Finally, we used Australia as a case study to demonstrate the benefits of combining the CLIMEX model of P. psidii s. l. with the distribution of potential Myrtaceae host plants, including threatened species and forestry plantations, to identify areas that are most at risk from the rust.
Thirty-two inoculated disks were placed in each of the base of seven 90 mm diameter plastic Petri dishes. A four by eight grid pattern with cells numbered from 1 to 32 printed on paper was fixed on the outside of each dish base to provide a unique identification number to each inoculated disk. Each Petri dish was then wrapped in foil and placed in one of seven different compartments of a uni-directional temperature gradient plate similar to that of Barbour and Racine  [37]. The Perspex compartments experienced the following average temperatures (standard deviation): 8. 8uC (0. 19), 11. 2uC (0. 20), 15. 6uC (0. 25), 19. 1uC (0. 35), 21. 8uC (0. 32), 26. 9uC (0. 38) and 29. 7uC (0. 74). The recorded temperature in each compartment was based on measurements taken over a three week period with a Hobo data logger (Onset Computer Corporation).
f. isotropic smoothers). Geographical distribution of Puccinia psidii s. l. The historical distribution of P. psidii s. l. used to build the CLIMEX model was assembled from several sources. Booth et al. [18]  reports nine point locations where the rust has been recorded in South America. Unpublished data on counties or locations where the rust has been recorded in Florida, California and Hawai'i were obtained from a range of collaborators (personal communications: Anne Marie La Rosa, United States Forest Service and Cherisa Coles, Janice Uchida and Mee Sook Kim, University of Hawai'i). Additional data on the distribution in Hawai'i were obtained from a roadside survey on the islands of Kaua'i, O'ahu, Maui and Hawai'i  [41]. The country-level occurrence records of P. psidii s. l. comprised in the CABI Distribution Maps of Plant Diseases  [42]  and the point distribution records indicated in Magarey et al.
Base climate data and CLIMEX model The primary base climatology used to build the model was the CliMond 109 climate normals centred on 1975 (CM10_1975H_V1_1)  [43]. We chose to use this hybrid dataset as it provided improved spatial resolution over the traditional 0. 5 degree climate datasets, and improved data quality. We attempted to supplement the available data for Hawai'i through the US National Climatic Data Centre and the United States Geological Survey, because the existing climate data is very scant, and on these islands the climatic gradients are extremely steep for both rainfall and temperature. While it was possible to supplement the data with additional temperature, and to a lesser extent rainfall records, it was impossible to locate any additional source of data variables to indicate atmospheric wetness (e.
The CLIMEX niche modelling package  [24, 25]  was used to create a model for P. psidii s. l. (Table  1 ). The model is based on previous work  [49] , in which the model stresses were fitted before location records in Japan, China and Australia had been collected. In the model presented in this paper, only the growth index parameters were adjusted to accommodate the observed germination responses of urediniospores of an Australian accession of P. psidii s. l. to a range of temperatures under experimental conditions. The Compare Locations function in CLIMEX calculates an annual index of climatic suitability, the Ecoclimatic Index (EI), which reflects the combined potential for population growth during favourable periods and survival during stressful periods (Equation  1 ). The annual Growth Index (GI A ) describes the potential for growth of the modelled organism as a function of average weekly soil moisture (Moisture Index; MI) and temperature (Temperature Index; TI) during favourable conditions only (Equation  2 ; GI W = TI W 6MI W ).
To avoid biasing the diversity counts within each cell, where the species epithet was blank (1 926 records) or 'sp' (940 records) in the extracted herbarium dataset, the species field was filled with 'sp. ' using the search and replace option in Microsoft Excel. The Myrtaceae species location records were then imported into ArcGIS 10. 3 (ESRI, Redlands, Ca. , USA) and spatially joined to the cell identifiers for the corresponding 109 climate data, before being reexported to Microsoft Excel, where the Remove Duplicates option was employed. A total of 121 377 non-unique combinations of genus, species and cell identifier were found and removed, leaving 59 814 taxonomically and geographically unique records. The unique records were re-imported into ArcGIS and spatially joined to the climate dataset.
The species richness for Myrtaceae in each 109 climate cell was then gauged by dividing the number of taxonomically unique records in each 109 climate grid cell by the projected area in km 2. Threatened Myrtaceae species. A map of the Myrtaceae species listed as threatened under the Australian Government Environment Protection and Biodiversity Conservation (EPBC) Act  [53]  that fall within the area where P. psidii s. l. was modelled as having an EI. 0 was generated as above. Hardwood plantations. The major hardwood (Myrtaceae) and mixed hardwood and softwood forest plantations present in Australia were extracted from the Australian National Forest Inventory for 2010  [54]. The dataset was projected using an Albers equal area projection to estimate areas for the forest plantations. These data were spatially intersected with the CLIMEX model results and the Australian State boundaries to estimate the areas of hardwood and mixed hardwood and softwood plantations climatically suitable for supporting the establishment of populations of P.
CLIMEX model The CLIMEX EI world map of P. psidii s. l. shows its preference for moist climates with moderate temperatures throughout the wet tropics and sub-tropics, and even extending into some cool regions with a mild Mediterranean climate (Fig. 4a ). In the native range, the regions of highest climatic suitability fall in Brazil and Paraguay in south-eastern South America, and also in a narrow band in the north-west of South America. The Caribbean and surrounding moist regions of Central America and the invaded areas of the USA are also highly climatically suitable. Its range appears limited by cold and dry stress in the south and western parts of Argentina, dry stress near the Rio Grande River in New Mexico and Texas in the USA, and by cold stress elsewhere in the USA (data not shown).
psidii s. l. In Brazil, for example, before resistant clones were deployed, P. psidii s. l. was reported to cause major damage in eucalyptus plantations, particularly in the first two years of planting  [62]. The existence of different pathotypes of P. psidii s. l. (e. g. [15, 16, 17] ) however, means that any attempt to manage the invasion risks using resistant hosts should, if possible, confirm that resistance is effective across the known pathotypes. In Australia, the key concerns associated with the recent invasion by P. psidii s. l. include the additional risks to native species and ecosystems important for conservation, as well as forestry Figure  5. Relative growth potential for Puccinia psidii s. l. as indicated by the CLIMEX annual Growth Index (GI A ) modelled using the CliMond CM10 1975H V1_1 climate dataset  [43].
psidii s. l. should be considered indicative, rather than prescriptive. For example, where P. psidii s. l. climatic suitability appears to diminish in the Australian Alps and in Tasmania, the climatic gradients are extremely steep in relation to the scale of the climate data used in this exercise, and due to the precision of the climatic dataset  [63]  there may be favourable microhabitats within the areas modelled as unsuitable.
Acknowledgments Annemarie La Rosa  ( USFS ) and  Janice Uchida  ( University of Hawai'i at Manoa ) provided valuable advice on the Hawai'ian survey. Alison Vaughan  and  David Cantrill  ( Victorian Herbarium ) provided distribution records for Australian Myrtaceae from the  Australian Virtual Herbarium , which is now part of the  Atlas of Living Australia. Suzy Perry ,  Fiona Giblin ,  Geoff Pegg  and  Heather Taylor  ( Department of Agriculture, Fisheries and Forestry Queensland ) provided distribution data for P. psidii s. l. in Queensland and  Paul Mahon  ( Office of Environment and Heritage NSW ) and  David Smith  ( Victoria Department of Primary Industries ) provided distribution information for NSW and Victoria, respectively. Adam Gerrand ,  Australian Bureau of Resource Sciences and Mijo Gavran of Australian Bureau of Agricultural  and  Resource Economics  and Sciences kindly supplied plantation inventory data.
Background: Several studies in colorectal cancer (CRC) indicate a relationship between tumour immune infiltrates and clinical outcome. We tested the utility of a digital pattern recognition-based image analysis (DPRIA) system to segregate tissue regions and facilitate automated quantification of immune infiltrates in CRC. Methods: Primary CRC with matched hepatic metastatic (n ¼ 7), primary CRC alone (n ¼ 18) and primary CRC with matched normal (n ¼ 40) tissue were analysed immunohistochemically. Genie pattern recognition software was used to segregate distinct tissue regions in combination with image analysis algorithms to quantify immune cells. Results: Immune infiltrates were observed predominately at the invasive margin. Quantitative image analysis revealed a significant increase in the prevalence of Foxp3 (Po0.
Isotype-matched mouse monoclonal antibodies were used as negative controls. Image analysis. Digital images of IHC-stained glass slides were acquired using a ScanScope digital scanner (Aperio ePathology Solutions, Vista, CA, USA). Images were viewed and organised using ImageScope viewer and Spectrum slide manager system, respectively (Aperio ePathology Solutions). Each scanned image was annotated manually for analysis (by HKA) to exclude areas of glass or poor sample quality. Genie pattern recognition software was used to generate Genie classifiers for the separation of areas of tumour, stroma, immune aggregates, non-neoplastic tissue and glass. First, the pattern recognition software was trained using a montage of example images that had been manually marked by the operator to distinguish the individual areas of interest.