dataesr/scientific-paragraphs-categorization huggingface dataset:
Each voxel in our discretization is then assigned a value of D based on this segmentation. To constrain the tumor growth to neuroanatomical boundaries, we set D to zero in the CSF. To simulate the increased diffusive capacity of gliomas in white matter, we set D in areas of gray matter to a value 1000 times less than in white matter. From our simulations we are able to visualize the tumor's volumetric growth within the patient's brain as shown in the serial, 3D images of Figure  1. 4D-spherical model-Applying Equation (2 but assuming spherical symmetry generates a full 4D simulation, but with growth only in the radial direction, creating a spherical tumor. We performed simulations for this simplified geometry using the built-in pdepe. m function in MATLAB. We parameterize these simulations with D and ρ values that are computed in the same way as the 4D-anatomical simulations but execute them within a homogenous spatial domain that, unlike the 4D-anatomical models, is not informed by patient-specific anatomy.
Image processing Computing a Days Gained score requires a volumetric segmentation of the tumor on two pre-treatment and one post-treatment T1Gd MRI scan. Spherically equivalent radii, which are used to parameterize each UVC and to compute Days Gained scores, can be computed from these volumes using Equation (1. We measured tumor volumes on T1Gd and T2 patient MRIs using semi-automated segmentation software developed in MATLAB. This software facilitates tumor volume measurement by employing a background subtraction algorithm that helps automate the tumor segmentation process by allowing the user to identify the tumor region with a polygon of interest. An automated edge detection algorithm then defines the imaging abnormality within the enclosed area. Although our segmentation pipeline relies on custom software not currently available to clinicians, there are several readily-available, commonly used clinical visualization tools available for performing this same kind of segmentation, including ImageJ  (25) , OsiriX  (26) , and Mipab  (27).
Statistical analyses To quantify the agreement between Days Gained scores computed from the three different models, we performed linear regression and Bland-Altman analyses  (9). We computed the coefficient of regression statistic in R version 2. 14. 1 and computed Bland-Altman biases and limits of agreement using MATLAB. For each set of Days Gained scores that we computed from our models, we performed iterative Kaplan-Meier analyses  (10)  to find an optimal Days Gained threshold that would maximally discriminate patients based on PFS and OS. We computed PFS as the interval between the patient's start of cytotoxic therapy and the confirmation of progression using standard criteria as gathered from radiology reports. We computed OS as the interval between the patient's date of diagnosis and their date of death.
We censored observations at the time of last follow-up if the outcome in question was not observed. To ensure that our analyses included a substantial number of patients with and without the observed outcome, we limited it to cases where both groups discriminated by the Days Gained threshold contained at least 20% of the total patient number. We used the "survival" package for R software version 2. 14. 1 (R Foundation for Statistical Computing, Vienna, Austria) to perform the Kaplan-Meier analyses (log-rank test). Once we identified the simplest model that significantly discriminated outcomes according to these Kaplan-Meier analyses, we validated its overall prognostic power using bootstrap validation  (28)  in R (version 2. 15. 1). We created training sets by randomly resampling the original dataset with replacement, then computed the optimal Days Gained threshold that discriminated the outcome in question.
We then applied this threshold to a Kaplan-Meier analysis of the original dataset and recorded the chi-squared statistic. After 10,000 iterations of this procedure, we averaged the resulting chi-squared statistics, then computed the corresponding p-value to validate our metric for discriminating PFS and OS outcomes. We performed a receiver operator curve (ROC) analysis to find the optimal Days Gained threshold that would distinguish our true progression and pseudoprogression cohorts. After identifying this cutoff, we performed a Welch's t-test to determine if the two groups had significantly different Days Gained scores and also assessed the positive and negative predictive value of the cutoff. To validate our approach more generally, we performed LOOCV (28), a validation method more suited to dichotomous tests and reduced datasets.
Acknowledgments Funding sources:  McDonnell Foundation  (JL,  RCR, KRS) Brain Tumor Funders Collaborative (MLN, RCR, KRS) National Institutes of Health  [ NS060752 ] (MLN, SA, AB, RS, TC, MMM, JKR, RCR, KRS) National Institutes of Health [CA16437] (CAB, LG, MMM, JKR,  RCR, KRS) National Institutes of Health [CA149237] (ADT) University of Washington James D. Murray Endowed Chair (KRS) University of Washington Academic Pathology Fund (RCR, KRS) We acknowledge the generous and unwavering support of Ellsworth (Buster)  C. Alvord ,  Jr. MD  ( 1923-2010 ) who is gravely missed. May his scientific legacy live on through the work reported here. We also acknowledge those who contributed to image analysis in this study:  Kellie Fontes ,  Jacob Gile ,  Alex Kim ,  Chantal Murthy , and  Clifford Rostomily.
The pivotal year 2000 We can divide thermal remote sensing of volcanic surfaces into two general classes: satellite-based and ground-based. Of these, ground-based can be further split into studies that use sensors capable of point-based measurements (i. e. , radiometers) and those capable of imaging (i. e. , thermal cameras). If we examine the literature database for these sensor groupings, we find approximately 200 papers were published between 1960 and 2005 in the international, peer-reviewed literature, as collated by  Harris (2012).
The publication time-line: satellite-based studies If we plot the number of publications per year from 1960 to 2005 we see that volcanic hot spot research using satellite-based sensors began in the mid-1960s and developed quite slowly (at a rate of 0. 2 publications per year) until 1985, when the rate of publication began to increase, attaining a rate of 2 publications per year between 1985 and 1992 (Fig. 1 ). Thereafter, the rate of publications continued to increase at a relatively steady rate of ~8 publications per year. Thus, by the year 2000, satellite remote sensing of volcanic hot spots had reached a degree of maturity, with the publication rate being high and steady. This maturity meant that a number of established methodologies were available for application to the new datasets thus creating a recipe book and background library that could be quickly applied to improved temporal, spatial, and/or spectral resolution datasets allowing quick start up and rapid progress.
(2011)  combining MODVOLC and the Robust Approach (RAT) to generate another, more sensitive, "hybrid" detection algorithm. Other detection systems similarly acquired operational status and extensive usage during the decade: (1) The VAST algorithm of  Harris et al. (1995) , as fully implemented by  Higgins and Harris (1997) , was used as part of a MODIS hot spot detection and enquiry system designed for Etna by  Ganci et al. (2011). VAST was also combined with MODVOLC, and various other permutations of the dual-band technique designed and tested during the 1990s, by  Hirn et al. (2008)  to generate MyVOL and MyMOD, two software packages for ingestion and processing of ASTER and MODIS data, respectively. (2) RAT, a system for volcano hot spot detection initially proposed by  Tramutoli (1998) , was applied by  Pergola et al.
(2001 Pergola et al. ( , 2004a Pergola et al. ( , 2004b Pergola et al. ( , 2008 Pergola et al. ( , 2009)) ,  Tramutoli et al. (2001), and Di Bello et al. (2004)  to AVHRR data for Etna. Progress in implementation of algorithm packages to detect and process volcano hot spots in satellite data was also reflected in the installation of a number of operational monitoring systems. Whereas Kaneko et al. (  2002 ) described a prototype system for tracking AVHRR-detected hot spots at Japanese volcanoes, GOES-based automated volcano hot spot detection and information dissemination (via email) was developed at the University of Hawaii alongside MODVOLC  (Harris et al. , 2002). Webley et al. (2008)  installed a system to allow on-reception hot spot detection and tracking in AVHRR data received for Central America using data acquired by the Instituto Nicaraguense de Estudios Territoriales (INETER) PC-based satellite receiver in Nicaragua.
Ganci et al. (2011)  closed out the decade by describing a system operational at INGV-Catania (HOTSAT) that used MODIS and SEVIRI data to detect and process hot spot data, in near-real-time, for Mt. Etna, with  Labazuy et al. (2010)  describing an internet-based plume and hot spot detection system (HOTVOLC) that processed on-reception SEVIRI data for all volcanoes within the sensor footprint (http://wwwobs. univ-bpclermont. fr/SO/ televolc/hotvolc/index. php). In addition to the automated systems that operated on data from one sensor, systems have also proliferated that integrate data from multiple systems flown on one or more satellites in a sensor-web approach. For example, a software system put into operational use during the past decade can perform autonomous onboard image scheduling, acquisition, and data processing for rapid analysis of volcanic events.
The Autonomous Sciencecraft Experiment (ACE) operates on data collected by the Earth Observing-1 (EO-1) spacecraft and has been used to rapidly acquire data, subset the region of interest, and differentiate styles of volcanic activity on the hour time scale, all of which would have taken days to perform manually  (Davies et al. , 2006). In addition, constellations of satellites are now being employed to rapidly detect increases in thermal activity and subsequently image the volcano with higher spatial and spectral resolution data. Such a system has been in place since 2004 for volcanoes in the northern Pacific from the Cascades in North America to the northern Kurile Islands in the western Pacific. The system relies on near-realtime monitoring using the 3. 5 μm channel of AVHRR developed in the late 1990s to detect a hot spot  (Dean et al.
, 1998). That system applies a rules-based algorithm to screen for errors and false positives. Detections are then used as scheduling triggers for the ASTER sensor and data are made available within hours of acquisition on the internet  (Ramsey et al. , 2004; Duda et al. , 2009). This system has increased the ASTER observational frequency to as short as 1-3 days for certain targets (Fig. 4 ) and is now integrated with the MODVOLC program in order to rapidly image active volcanic targets worldwide.
Inventories MODVOLC-type systems, as well as the GOES-MSG-MTSAT geostationary constellation, allow global coverage at high-temporal frequencies. Furthermore, some databases now extend over decades (data bases for MODIS, GOES and AVHRR now span 11, 14, and 32 years, respectively). Thus, studies examining spatial and temporal trends and patterns in spectral radiance (and its derivable products) have increased in the first decade of the 21st century. High-temporal resolution IR data for any volcano on the globe are now available from GOES-MSG-MTSAT, allowing detailed time series to be produced for any (cloud-free) short-lived and transient events, such as fire fountains (e. g. ,  Ganci et al. , 2011; Gouhier et al. , 2011; Vicari et al. , 2011). At the same time, the longevity of data sets such as those provided by AVHRR, TM and MODIS allow long-term trends in volcanic activity to be tracked and understood (e.
g. ,  Wooster, 2001; Harris et al. , 2011; Coppola et al. , 2012). Time series analysis span studies of single volcanoes, through patterns in regional activity, to global inventories. The ease with which data can be downloaded also makes time series easy to assemble. These studies are also based on time series spanning periods lasting a few days, through eruptions (or eruption sequences) that last months to years (and even decades). For example,  Harris and Ripepe (2007)  used MODVOLC data to show that there was a near-immediate heat flux response to the magnitude 6. 4 regional earthquake at the persistently active (open systems) of Merapi and Semeru (both located on Java) during the nine-day-long response itself. Kaneko et al. (2006)  used MODIS data spanning January 2004 to April 2005, with ancillary gas and seismic data to track the 2004-2005 activity at Asama volcano, Japan.
The year 2000 The centerpiece of our presentation in 2000 was a ground-based radiometer, set up in such a way that it was protected from gas and weather effects, viewing the target from within a sealed case through a selenium-germanium-arsenide window  (Harris et al. , 2005). They were also robust, operational and capable of long-term deployment to allow time series data to be collected (e. g. ,  Marchetti and Harris, 2008) , as well as capture the rare but violent events (e. g. ,  Rosi et al. , 2006; Harris et al. , 2008; Ripepe et al. , 2008)  such as the 5 April 2003 explosion at Stromboli (Fig. 6 ). Between 1999 and 2004, this radiometer system was deployed at ten volcanoes viewing eight different activity types: Stromboli (explosions), Santiaguito (silicic lava flow), Kilauea (skylights), Pacaya (degassing), Poas (crater lake), Masaya (gas puffing), Villarrica (gas puffing), Fuego (explosions), Erta Ale (lava lake), and Soufriere Hills (lava dome).
Satellite-based technology in 2020 As we again look ahead to predict where IR-based technology will advance volcanology during the next decade, we foresee satellitebased instruments and technology continuing as the primary synoptic tool. However, most of the new instruments scheduled for launch are focused on atmospheric science rather than geology/geophysical community. Furthermore, the development will continue to focus mainly on lower spatial resolution operational infrared systems in both geostationary (GEO) and polar low Earth (LEO) orbits (Table  4 ). For example, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor in GEO orbit has continued the tradition of hightemporal/low spatial resolution (15 min with 3 km/pixel) data and has the capability to detect hotspots. The planned Geostationary Satellite system (GOES-R) satellite will be greatly improved over past GOES sensor systems, including 11 more spectral channels (with 7 of these being in the mid IR and thermal IR wavelengths), an improved temporal resolution (5 min), and an improved spatial resolution in the IR (2 km).
, 2009). One of these satellites, due for launch within the next year, will carry a TIR instrument called the Thermal Hyperspectral Imager (THI). The THI is designed to acquire calibrated radiance images with a 20 wavenumber spectral resolution, equivalent to ~100 wavelength bands between 7 μm and 13 μm  ( Crites et al. , 2011). Another area that should see improvement over the current decade is the use of available data for new and more accurate algorithms designed for rapid data analysis and parameterization of volcanic effusive phenomena. For example, data acquired synchronously in the TIR and MIR wavelength regions could be exploited to more accurately describe the range of surface temperatures and their percentages within thermally-anomalous pixels. Infrared radiant flux data that are both accurate, and not saturated, provide the best possibility for determining lava temperature, area, discharge rate, and constraints on the composition of the flow surface.
Furthermore, improvements can potentially be made in the temporal resolution of sensors already (or soon to be) in orbit by way of changes to flight operational software, which would thus limit the need to launch new systems with improved resolution. For example, image subsetting in near-real time using scan modes such as the Super Rapid Scan Operations (SRSO) for the GOES sensors provides data of transient phenomena such as hurricanes every minute (compared to every 15 min in normal scan mode). However, this type of acquisition mode does exclude all other areas of the scene except for that of the target, and has not yet been applied to volcanic eruptions (as far as we can tell). The SRSO mode has enormous potential for volcanological applications such as tracking dynamic ash plumes or thermal output.
If this style of observation could be quickly and automatically initiated, it would capture the detailed transient thermal features of the initial stages of large eruptions, or transient, rapidlyevolving and/or short-lived eruptions, such as fire fountain events. Similarly, if initiated prior to eruption (perhaps targeted because of ground-based triggers such as increased heat flux detected from permanently deployed IR cameras) a high data rate scan mode could capture event onset with 60-second precision. Future application of onboard processing software such as ACE on EO-1, rapid-response scanning modes such as SRSO on the GOES satellites, and sensorweb approaches such as ASTER-URP will provide the most rapid response to volcanic events and the quickest dissemination of data to scientists and emergency managers while the science community waits for satellite-based sensors designed specifically for the unique needs of accurate volcanic observations.
It seems obvious to say that we expect the collation of thermal camera based studies in the volcanological journals (shown in Table  1 ) to be at least twice as long by 2020. But what could be new in the next decade? On answering this question, we see the potentially new data and capabilities sub-dived into three primary themes: (1) improved spectral resolution; (2) new airborne technology; and (3) fusion of the data with to improve detection and measurement capabilities. The improved spatial and/or spectral resolution of MIR and TIR instruments provide both the ability to resolve a larger number of high temperature sub-pixel temperature components as well as the compositional and textural variation of those deposits. This was documented by  Flynn et al. (2000)  who used data from a hyperspectral field spectrometer in the 2.
0 to 2. 5 μm wavelength range to constrain the number of high temperature components on an active basalt flow. Wright and Flynn (2003)  followed this study with a similar objective. They used the statistical distribution of temperatures measured in the 76,800 pixels of a TIR camera image to document at least seven dominant temperatures in active basalt flow field. We expect future studies in this area will be possible with rapid data processing, the results of which will be used for more accurate modeling of lava flow emplacement and validation of spaceborne data. An increase in the number of wavelength bands in the TIR region allows for more precise temperature extractions because the derivation of radiant temperature is intimately linked to the accuracy by which one can estimate the surface emissivity.
This move toward hyperspectral TIR emissivity data is also critical for accurate determine surface and plume composition. Emissivity is a fundamental wavelength-dependant property controlled by the vibrational (e. g. , bending and stretching) frequencies of the atomic bonds of the material. For example, in silicate minerals, the Si\O\Al bonds determine the magnitude and wavelength location of the emissivity absorption in the TIR region. These values in turn control the rate of IR emission from the surface and hence affect the cooling rate, degree of crust formation, and flow length for example. By separating the emissivity for the complex thermal structure of volcanic deposits, it can also be used to estimate the mineralogical and textural components of the targeted surface (e. g. ,  Ramsey and Fink, 1999; Christensen et al.
, 2008). The sensor has a unique modified Dyson spectrometer optical design that allows a very compact and optically fast system (see  Hall et al. , 2008). The focal plane array is a cooled HgCdTe composition with proven lineage in the airborne TIR instrument history. HyTES also relies on a Dyson-inspired optical design with a similarly large field of view, but with 256 spectral channels between 7. 5 μmt o1 2μm. Another key difference between MAGI and HyTES is that HyTES uses a quantum well infrared photodetectors (QWIPs) focal plane array. This FPA requires more significant cooling (40 °C versus 70 °C) than the HgCdTe arrays but generally has a higher spatial uniformity. Both instruments are being designed as potential test beds for future spaceborne TIR instruments such as HyspIRI. A final aspect of rapidly growing technology and data analysis in the next decade is the concept of data fusion, or the merging of high spatial resolution thermal data (from ground-based cameras and airborne scanners, for example) with topographic data acquired by tripod and aircraft mounted LIDAR shows great promise (e.
Unfortunately, the next decade does not hold the promise of the last in terms of spaceborne data. The exception to that will be the next generation of operation systems such as GOES and AVHRR, which will be launched before 2020. However, our pessimistic view does hold true for scientific (non-operational) sensors such as the HyspIRI mission, which offers both high spatial and temporal resolution data in the TIR as well as saturation temperatures of ~770 °C in the TIR and ~1470 °C in the single MIR channel. Despite being recommended as a secondtier mission in the Earth Science Decadal Survey, the proposed launch date of 2013 for HyspIRI was recently delayed by NASA up to as many as ten years due to a priority refocus to climate-specific measurements. This delay will now certainly cause a data continuity gap after the end of ASTER, and will severely limit the availability (and continuity) of high resolution multispectral TIR data from space.
Unfortunately, there will be little doubt that we will be talking about a "future" HyspIRI mission in 2020 when we write our next decadal review. Furthermore, the next generation of Landsat (the Landsat Data Continuity Mission or LCDM) for years was scheduled to launch with no TIR capability-for the first time since Landsat 4 in 1982. After much debate and input from the scientific community, this decision was changed and the Thermal InfraRed Sensor (TIRS) was added, which will provide 100 m data in two spectral bands. Sadly, compared to the five spectral bands of ASTER data at 90 m spatial resolution or one 60 m band of TIR data provided by ETM+, TIRS cannot be seen as an improvement in any way from what ASTER and ETM + provided over a decade earlier. Perhaps small satellite sensors ("small sats") launched by consortiums of universities and industry will help augment these looming data gaps caused by the ever-changing priorities and budgets of governmental space agencies.
With a looming gap in spaceborne TIR data, we do envision a more rapid expansion of many of the ground-based technologies, however. Much of this will be focused on technology to advance rapid, real- time imaging with sub-second temporal resolution with everincreasing spatial resolution. These data will become routine input into integrated monitoring, modeling, and hazard appraisal. For example, as thermal infrared camera data become less expensive and easier to use, these sensors will be expanded into multispectral devices and placed around active volcanoes in order to capture spatial patterns of temperature, composition, and surface texture at the second-to-minute time scale (e. g. ,  Ramsey, 2009; Calvari et al. , 2011). Similar sensors are now being developed for possible inclusion into commercial aircraft for real-time ash hazard avoidance  (Prata, 2009).