Pterulaceae was formally proposed to group six coralloid and dimitic genera: Actiniceps (= Dimorphocystis) , Allantula , Deflexula , Parapterulicium , Pterula, and Pterulicium . Recent molecular studies have shown that some of the characters currently used in Pterulaceae do not distinguish the genera. Actiniceps and Parapterulicium have been removed, and a few other resupinate genera were added to the family. However, none of these studies intended to investigate the relationship between Pterulaceae genera. In this study, we generated 278 sequences from both newly collected and fungarium samples. Phylogenetic analyses supported with morphological data allowed a reclassification of Pterulaceae where we propose the introduction of Myrmecopterula gen. nov. and Radulomycetaceae fam. nov., the reintroduction of Phaeopterula , the synonymisation of Deflexula in Pterulicium, and 53 new combinations. Pterula is rendered polyphyletic requiring a reclassification; thus, it is split into Pterula , Myrmecopterula gen. nov., Pterulicium and Phaeopterula . Deflexula is recovered as paraphyletic alongside several Pterula species and Pterulicium, and is sunk into the latter genus. Phaeopterula is reintroduced to accommodate species with darker basidiomes. The neotropical Myrmecopterula gen. nov. forms a distinct clade adjacent to Pterula , and most members of this clade are associated with active or inactive attine ant nests. The resupinate genera Coronicium and Merulicium are recovered in a strongly supported clade close to Pterulicium . The other resupinate genera previously included in Pterulaceae , and which form basidiomes lacking cystidia and with monomitic hyphal structure ( Radulomyces , Radulotubus and Aphanobasidium ), are reclassified into Radulomycetaceae fam. nov. Allantula is still an enigmatic piece in this puzzle known only from the type specimen that requires molecular investigation. A key for the genera of Pterulaceae and Radulomycetaceae fam. nov. is also provided here.

Contouring (segmentation) of Organs at Risk (OARs) in medical images is required for accurate radiation therapy (RT) planning. In current clinical practice, OAR contouring is performed with low levels of automation. Although several approaches have been proposed in the literature for improving automation, it is difficult to gain an understanding of how well these methods would perform in a realistic clinical setting. This is chiefly due to three key factors - small number of patient studies used for evaluation, lack of performance evaluation as a function of input image quality, and lack of precise anatomic definitions of OARs. In this paper, extending our previous body-wide Automatic Anatomy Recognition (AAR) framework to RT planning of OARs in the head and neck (H&N) and thoracic body regions, we present a methodology called AAR-RT to overcome some of these hurdles. AAR-RT follows AAR's 3-stage paradigm of model-building, object-recognition, and object-delineation. Model-building: Three key advances were made over AAR. (i) AAR-RT (like AAR) starts off with a computationally precise definition of the two body regions and all of their OARs. Ground truth delineations of OARs are then generated following these definitions strictly. We retrospectively gathered patient data sets and the associated contour data sets that have been created previously in routine clinical RT planning from our Radiation Oncology department and mended the contours to conform to these definitions. We then derived an Object Quality Score (OQS) for each OAR sample and an Image Quality Score (IQS) for each study, both on a 1-to-10 scale, based on quality grades assigned to each OAR sample following 9 key quality criteria. Only studies with high IQS and high OQS for all of their OARs were selected for model building. IQS and OQS were employed for evaluating AAR-RT's performance as a function of image/object quality. (ii) In place of the previous hand-crafted hierarchy for organizing OARs in AAR, we devised a method to find an optimal hierarchy for each body region. Optimality was based on minimizing object recognition error. (iii) In addition to the parent-to-child relationship encoded in the hierarchy in previous AAR, we developed a directed probability graph technique to further improve recognition accuracy by learning and encoding in the model "steady" relationships that may exist among OAR boundaries in the three orthogonal planes. Object-recognition: The two key improvements over the previous approach are (i) use of the optimal hierarchy for actual recognition of OARs in a given image, and (ii) refined recognition by making use of the trained probability graph. Object-delineation: We use a kNN classifier confined to the fuzzy object mask localized by the recognition step and then fit optimally the fuzzy mask to the kNN-derived voxel cluster to bring back shape constraint on the object. We evaluated AAR-RT on 205 thoracic and 298 H&N (total 503) studies, involving both planning and re-planning scans and a total of 21 organs (9 - thorax, 12 - H&N). The studies were gathered from two patient age groups for each gender - 40-59 years and 60-79 years. The number of 3D OAR samples analyzed from the two body regions was 4301. IQS and OQS tended to cluster at the two ends of the score scale. Accordingly, we considered two quality groups for each gender - good and poor. Good quality data sets typically had OQS ≥ 6 and had distortions, artifacts, pathology etc. in not more than 3 slices through the object. The number of model-worthy data sets used for training were 38 for thorax and 36 for H&N, and the remaining 479 studies were used for testing AAR-RT. Accordingly, we created 4 anatomy models, one each for: Thorax male (20 model-worthy data sets), Thorax female (18 model-worthy data sets), H&N male (20 model-worthy data sets), and H&N female (16 model-worthy data sets). On "good" cases, AAR-RT's recognition accuracy was within 2 voxels and delineation boundary distance was within ∼1 voxel. This was similar to the variability observed between two dosimetrists in manually contouring 5-6 OARs in each of 169 studies. On "poor" cases, AAR-RT's errors hovered around 5 voxels for recognition and 2 voxels for boundary distance. The performance was similar on planning and replanning cases, and there was no gender difference in performance. AAR-RT's recognition operation is much more robust than delineation. Understanding object and image quality and how they influence performance is crucial for devising effective object recognition and delineation algorithms. OQS seems to be more important than IQS in determining accuracy. Streak artifacts arising from dental implants and fillings and beam hardening from bone pose the greatest challenge to auto-contouring methods.

Low-cost, low-power X-band phased array radar (LPAR) is an enabling technology for future deployment of distributed short-range radar networks. Such networks offer the potential for superior and lower altitude surveillance of atmospheric and airborne events compared with today's larger, long range national radar networks. Two dimensionally steered (phase-phase steering, without motors or other moving parts) phased array radars are complex systems comprising multiple subsystems including several thousand transmit/receive (T/R) channels, beam steering computers, thermal management. Owing to this complexity and the associated cost, phased array technology has not historically been used in weather and air traffic control radars. Competition for the frequency spectrum traditionally reserved for long-range radars is motivating the search for new approaches to national air surveillance; this has motivated R&D investment in two-dimensional X-band LPAR over the past decade, to the point where prototype systems are now emerging in several application settings including, for the first time, the university research setting. Two-dimensional high-speed (inertia-less) beam steering combined with dual polarization, programmable/adaptive waveforms, and the ability to combine multiple radars into networks is leading to new atmospheric science research opportunities related to hazardous storm forecasting and response, understanding cloud physics, water resource management, monitoring the movement and dispersal of hazardous plumes, and other areas.

Contouring of the organs at risk is a vital part of routine radiation therapy planning. For the head and neck (H and N) region, this is more challenging due to the complexity of anatomy, the presence of streak artifacts, and the variations of object appearance. In this paper, we describe the latest advances in our Automatic Anatomy Recognition (AAR) approach, which aims to automatically contour multiple objects in the head and neck region on planning CT images. Our method has three major steps: model building, object recognition, and object delineation. First, the better-quality images from our cohort of H and N CT studies are used to build fuzzy models and find the optimal hierarchy for arranging objects based on the relationship between objects. Then, the object recognition step exploits the rich prior anatomic information encoded in the hierarchy to derive the location and pose for each object, which leads to generalizable and robust methods and mitigation of object localization challenges. Finally, the delineation algorithms employ local features to contour the boundary based on object recognition results. We make several improvements within the AAR framework, including finding recognition-error-driven optimal hierarchy, modeling boundary relationships, combining texture and intensity, and evaluating object quality. Experiments were conducted on the largest ensemble of clinical data sets reported to date, including 216 planning CT studies and over 2,600 object samples. The preliminary results show that on data sets with minimal (<4 slices) streak artifacts and other deviations, overall recognition accuracy reaches 2 voxels, with overall delineation Dice coefficient close to 0.8 and Hausdorff Distance within 1 voxel.

Algorithms for image segmentation (including object recognition and delineation) are influenced by the quality of object appearance in the image and overall image quality. However, the issue of how to perform segmentation evaluation as a function of these quality factors has not been addressed in the literature. In this paper, we present a solution to this problem. We devised a set of key quality criteria that influence segmentation (global and regional): posture deviations, image noise, beam hardening artifacts (streak artifacts), shape distortion, presence of pathology, object intensity deviation, and object contrast. A trained reader assigned a grade to each object for each criterion in each study. We developed algorithms based on logical predicates for determining a 1 to 10 numeric quality score for each object and each image from reader-assigned quality grades. We analyzed these object and image quality scores (OQS and IQS, respectively) in our data cohort by gender and age. We performed recognition and delineation of all objects using recent adaptations [8, 9] of our Automatic Anatomy Recognition (AAR) framework [6] and analyzed the accuracy of recognition and delineation of each object. We illustrate our method on 216 head & neck and 211 thoracic cancer computed tomography (CT) studies.

Abstract This study compares energy spectra of the multiple electron beams of individual radiotherapy machines, as well as the sets of spectra across multiple matched machines. Also, energy spectrum metrics are compared with central‐axis percent depth‐dose (PDD) metrics. Methods A lightweight, permanent magnet spectrometer was used to measure energy spectra for seven electron beams (7–20 MeV) on six matched Elekta Infinity accelerators with the MLCi2 treatment head. PDD measurements in the distal falloff region provided R 50 and R 80–20 metrics in Plastic Water®, which correlated with energy spectrum metrics, peak mean energy (PME) and full‐width at half maximum (FWHM). Results Visual inspection of energy spectra and their metrics showed whether beams on single machines were properly tuned, i.e., FWHM is expected to increase and peak height decrease monotonically with increased PME. Also, PME spacings are expected to be approximately equal for 7–13 MeV beams (0.5‐cm R90 spacing) and for 13–16 MeV beams (1.0‐cm R90 spacing). Most machines failed these expectations, presumably due to tolerances for initial beam matching (0.05 cm in R 90; 0.10 cm in R 80–20) and ongoing quality assurance (0.2 cm in R 50). Also, comparison of energy spectra or metrics for a single beam energy (six machines) showed outlying spectra. These variations in energy spectra provided ample data spread for correlating PME and FWHM with PDD metrics. Least‐squares fits showed that R 50 and R 80–20 varied linearly and supralinearly with PME, respectively; however, both suggested a secondary dependence on FWHM. Hence, PME and FWHM could serve as surrogates for R 50 and R 80–20 for beam tuning by the accelerator engineer, possibly being more sensitive (e.g., 0.1 cm in R 80–20 corresponded to 2.0 MeV in FWHM). Conclusions Results of this study suggest a lightweight, permanent magnet spectrometer could be a useful beam‐tuning instrument for the accelerator engineer to (a) match electron beams prior to beam commissioning, (b) tune electron beams for the duration of their clinical use, and (c) provide estimates of PDD metrics following machine maintenance. However, a real‐time version of the spectrometer is needed to be practical.

Truffle species within the /tarzetta-geopyxis lineage share smooth, globose, hyaline spores, but differ in the amount of convolution of hymenia in ascomata. The relationships among truffle species in this lineage have historically been confused. Phylogenetic analyses of the ITS and 28S nuclear ribosomal DNA from recently collected members of the /tarzetta-geopyxis lineage from Asia, Austral Asia, North America, and South America prompted a reinvestigation of species and generic limits in the truffle genera Hydnocystis, Paurocotylis, and Stephensia. Our analyses support emendations of Hydnocystis and Paurocotylis, abandonment of Stephensia and the resurrection of the genus Densocarpa. Nomenclatural changes include the transfer of Stephensia bombycina to Hydnocystis, the transfer of Hydnocystis singeri and Stephensia bynumii to Paurocotylis, the reinstatement of Densocarpa for Stephensia shanori and transfer of Stephensia crocea to Densocarpa. This is the first detection of the genus Paurocotylis in the Americas. We describe three new species, Hydnocystis transitoria from North America, Paurocotylis patagonica from South America, and Paurocotylis watlingii from Australia. Our work highlights the unexplored diversity, morphological plasticity, and remaining taxonomic problems among truffles in the /tarzetta-geopyxis lineage.

Early diverging taxa of Ascomycota and Basidiomycota share similarities in subcellular characters of the spindle pole body (SPB), nuclear division, and septal pore apparatus, but our understanding of character evolution is incomplete because of the limited number of structural studies within the earliest diverging subphyla of Dikarya, Taphrinomycotina and Pucciniomycotina. Two species of Helicogloea (Atractiellomycetes) were analyzed for these characters and provide data on SPB and nuclear division for an additional class of Pucciniomycotina. A detailed analysis of septal pore apparatus for the Helicogloea species permits comparisons with those of other Pucciniomycotina and Ascomycota. The endogenous origin of hyphal branches is shown to occur in a third class of Pucciniomycotina. The full set of characters supports a close relationship between Atractiellomycetes and Pucciniomycetes.

Manual contouring in RT planning of organs at risk (OARs) has been prone to significant variability due to a lack of standardized object definitions, leading to imprecision. Although clinical guidelines have been made available, ambiguities in object definitions still exist. Suboptimal object quality and image quality can also significantly impact the quality of object contouring. These issues are particularly problematic when creating robust object models for purposes of auto-contouring. As such, we present more precise definitions of selected OARs in the neck and thorax as an extension of existing guidelines. A prevailing issue is evaluation of auto-contouring methods as a function of input image quality. We propose a new approach to assess the impact of object and image quality upon auto-contouring results. We hypothesize that OAR manual contours still have significant variability from existing guidelines, and object and image quality impact auto-contouring. We first developed precise and computable definitions of the neck and thorax body regions based on anatomical considerations. We then developed precise standardized definitions of a set of key OARs in these body regions by extending object definitions available from recent guidelines. Next, we retrospectively created a database of CT images and manually drawn contours obtained from RT planning in 216 head and neck cancer patients and 200 thoracic cancer patients. A reader examined each image and assigned a quality grade to each 3D object in the image based on a set of pre-specified criteria which included: deviation in body posture and object intensity; presence of image noise, streak artifacts, object shape distortion, and pathology; and lack of object contrast. Using logical predicates, we designed an algorithm to map these quality grades to a numeric quality score for each object in each CT scan, as well as for the scan. Precise object definitions for 11 objects in the neck and 11 objects in the thorax were created. Per our standardized definitions, among all objects in the neck studies, 33.8% were acceptable without modification, 46.9% required minor changes, and 19.3% needed major editing. For the thorax, these rates were 10.5%, 56.17%, and 33.33%, respectively. ∼1% of the scans, considering all objects in each scan, were of high quality as per the above criteria. Results of analysis of an auto-contouring software as a function of the image quality numeric score will be presented at the conference. Precise standardized object definitions are essential to ensure high quality of OAR contours for RT planning. Yet, currently available guidelines are still ambiguous as borne out from our analysis. Suboptimal image quality may also significantly impact the quality of object contouring. We have arrived at more precise definitions of a set of objects in the neck and thorax, and developed a new approach to assess the impact of object and image quality upon auto-contouring results.

Manual contouring leads to inefficiency, variability, lack of standardization, hampered throughput, and inaccuracy in radiotherapy treatment planning, thus limiting widespread adoption of adaptive planning. To overcome current segmentation challenges, we designed two dichotomous operations – object recognition (or localization) and object delineation. Recognition affords encoding rich prior anatomic information, such as object relationships, which leads to generalizable methods and mitigation of segmentation challenges. We present a novel system built around the above principles and hypothesize that it will have high accuracy (within 3 voxels) for anatomy recognition in the head and neck (H&N) body region. Our method has three major steps: model building, object recognition, and object delineation. We focus on the first two steps. 1. Model building. We assessed planning CT image data (voxel size: 1 x 1 x 2 mm3 to 1 x 1 x 3 mm3) and clinical contours (10 objects per patient) from 216 patients (including men and women from two age groups [40-59 yrs and 60-79 yrs]) treated with concurrent chemoradiation for H&N cancer. We developed precise and computable definitions of the neck body region and of each object by adapting contouring consensus guidelines. We manually amended all contours to satisfy these definitions. Our method builds a population fuzzy model for each object by aligning and shape-based averaging of all samples of the object. It then creates an anatomy model of the whole H&N region wherein the object models are organized in a hierarchical tree, and parent-to-child relationships are computed and encoded. Information about the size and intensity variation of each object is also encoded into the H&N anatomy model. An optimal hierarchical tree is then computed that yields the best recognition accuracy among all possible tree arrangements. 2. Object recognition. The object at the root of the tree is localized first. Other objects are then recognized by following the tree, making use of the parent-to-child relationship in location, and fine-tuning the location based on image information. Whereas skin, mandible, parotid glands, submandibular glands, and spinal cord were localized within 1-3 voxels, esophagus (∼4 voxels), supraglottic larynx (∼6 voxels), and orohypopharynx (∼7 voxels) were more challenging. The quality and consistency of the clinical contours for these three objects were highly variable, and as a result, the models were not as reliable. High-quality models are necessary for achieving high accuracy in object localization in the H&N region. Hence, precise object definitions and contours drawn adhering to these definitions are essential. Clinical contours for most objects required substantial editing. With high-quality models, localization of objects within 1-2 voxels seems feasible. This can optimize efficiency and accuracy to facilitate personalized adaptive radiotherapy planning.

A Dacryopinax species that was cultured in Costa Rica and fruited in the laboratory provided DNA for the first sequenced genome for the Dacrymycetes. Here we characterize the isolate morphologically and cytologically and name it D. primogenitus Molecular sequences from the nuclear large subunit gene and internal transcribed spacer indicated that it is closely related to the South American D. indacocheae with which it agrees structurally. Both species form conidia on the basidiocarp, and D primogenitus also forms them on the mycelium. Unlike previous reports for the Dacrymycetales postmeiotic nuclear division results in uninucleate basidiospores and six residual nuclei in the basidium after basidiospore discharge. Ultrastructural analysis shows the characteristic septal-pore apparatus for the class and endogenous origin of the epibasidia/sterigmata, which may be a common occurrence in Dacrymycetes and the early diverging orders of its sister class, the Agaricomycetes.

Purpose: The purpose of this work was to adapt a lightweight, permanent magnet electron energy spectrometer for the measurement of energy spectra of therapeutic electron beams.Methods: An irradiation geometry and measurement technique were developed for an approximately 0.54-T, permanent dipole magnet spectrometer to produce suitable latent images on computed radiography (CR) phosphor strips. Dual-pinhole electron collimators created a 0.318-cm diameter, approximately parallel beam incident on the spectrometer and an appropriate dose rate at the image plane (CR strip location). X-ray background in the latent image, reduced by a 7.62-cm thick lead block between the pinhole collimators, was removed using a fitting technique. Theoretical energy-dependent detector response functions (DRFs) were used in an iterative technique to transform CR strip net mean dose profiles into energy spectra on central axis at the entrance to the spectrometer. These spectra were transformed to spectra at 95-cm source to collimator distance (SCD) by correcting for the energy dependence of electron scatter. The spectrometer was calibrated by comparing peak mean positions in the net mean dose profiles, initially to peak mean energies determined from the practical range of central-axis percent depth-dose (% DD) curves, and then to peak mean energies that accounted for how the collimation modified the energy spectra (recalibration). The utility of the spectrometer was demonstrated by measuring the energy spectra for the seven electron beams (7-20 MeV) of an Elekta Infinity radiotherapy accelerator.Results: Plots of DRF illustrated their dependence on energy and position in the imaging plane. Approximately 15 iterations solved for the energy spectra at the spectrometer entrance from the measured net mean dose profiles. Transforming those spectra into ones at 95-cm SCD increased the low energy tail of the spectra, while correspondingly decreasing the peaks and shifting them to slightly lower energies. Energy calibration plots of peak mean energy versus peak mean position of the net mean dose profiles for each of the seven electron beams followed the shape predicted by the Lorentz force law for a uniform z-component of the magnetic field, validating its being modeled as uniform (0.542 +/- 0.027 T). Measured Elekta energy spectra and their peak mean energies correlated with the 0.5-cm (7-13 MeV) and the 1.0-cm (13-20 MeV) R-90 spacings of the %DD curves. The full-width-half-maximum of the energy spectra decreased with decreasing peak mean energy with the exception of the 9-MeV beam, which was anomalously wide. Similarly, R80-20 decreased linearly with peak mean energy with the exception of the 9 MeV beam. Both were attributed to suboptimal tuning of the high power phase shifter for the recycled radiofrequency power reentering the traveling wave accelerator.Conclusions: The apparatus and analysis techniques of the authors demonstrated that an inexpensive, lightweight, permanent magnet electron energy spectrometer can be used for measuring the electron energy distributions of therapeutic electron beams (6-20 MeV). The primary goal of future work is to develop a real-time spectrometer by incorporating a real-time imager, which has potential applications such as beam matching, ongoing beam tune maintenance, and measuring spectra for input into Monte Carlo beam calculations. (C) 2015 American Association of Physicists in Medicine.

PREMISE OF THE STUDYThe earliest eukaryotes were likely flagellates with a centriole that nucleates the centrosome, the microtubule-organizing center (MTOC) for nuclear division. The MTOC in higher fungi, which lack flagella, is the spindle pole body (SPB). Can we detect stages in centrosome evolution leading to the diversity of SPB forms observed in terrestrial fungi? Zygomycetous fungi, which consist of saprobes, symbionts, and parasites of animals and plants, are critical in answering the question, but nuclear division has been studied in only two of six clades.METHODSUltrastructure of mitosis was studied in Coemansia reversa (Kickxellomycotina) germlings using cryofixation or chemical fixation. Character evolution was assessed by parsimony analysis, using a phylogenetic tree assembled from multigene analyses.KEY RESULTSAt interphase the SPB consisted of two components: a cytoplasmic, electron-dense sphere containing a cylindrical structure with microtubules oriented nearly perpendicular to the nucleus and an intranuclear component appressed to the nuclear envelope. Markham's rotation was used to reinforce the image of the cylindrical structure and determine the probable number of microtubules as nine. The SPB duplicated early in mitosis and separated on the intact nuclear envelope. Nuclear division appears to be intranuclear with spindle and kinetochore microtubules interspersed with condensed chromatin.CONCLUSIONSThis is the sixth type of zygomycetous SPB, and the third type that suggests a modified centriolar component. Coemansia reversa retains SPB character states from an ancestral centriole intermediate between those of fungi with motile cells and other zygomycetous fungi and Dikarya.

A novel analytical method is presented for evaluating the electrical performance of a radome for a dual-polarized phased-array antenna under rain conditions. Attenuation, reflections, and induced cross polarization are evaluated for different rainfall conditions and radome types. The authors present a model for estimating the drop size distribution on a radome surface based on skin surface material, area, inclination, and rainfall rate. Then, a multilayer radome model based on the transmission-line-equivalent circuit model is used to characterize the radome’s scattering parameters. Numerical results are compared with radar data obtained in the Next Generation Weather Radar (NEXRAD) and Collaborative Adaptive Sensing of the Atmosphere (CASA) systems, and good agreement is found.

A Raytheon-developed dual polarization X-band active phased array radar has been designed and is being tested for use as a multi-mission, weather and surveillance, radar. The radar is optimized for integration into existing networks with the capability of being deployed on existing telecom, turbine or building infrastructures. The partnership between Raytheon and CASA brings together years of expertise to meet future requirements of en route and terminal air surveillance and weather remote sensing. This paper provides an update to this Raytheon dual polarization active phased array radar technology.

Operational weather radars in the U.S. and other countries in the world are challenged in providing low-altitude observations of rainfall due to the Earth's curvature and their deployment in “sparse” networks spaced hundreds of km apart. Given this limitation, work is underway to explore the feasibility of “dense” networks of small X-band radars. One approach developed by a student team from the U.S. Engineering Research Center for Collaborative and Adaptive Sensing of the Atmosphere (CASA) uses low-cost networks of simple, single-polarization radars that are not dependent on existing infrastructure, operating using solar energy and ad-hoc wireless networks, providing gap-filling data with improved temporal and spatial resolution. This “off-the-grid” (OTG) concept is one that might offer a means to monitor rainfall and provide useful data where it is not feasible or cost-effective to deploy more costly and more accurate radars. This paper describes the OTG concept and design, and presents examples of collected data and respective comparisons from this OTG network with measurements from an S-band NEXRAD radar as well as rainfall data from a set of rain gauges located in Puerto Rico. Results show that CASA OTG radars can provide improved spatial and temporal rainfall estimates with consistent or smaller estimated errors when compared to the S-band radar. End user validation was demonstrated in collaboration with the U.S. National Weather Service during system deployment for the XXI Central American and Caribbean Games celebrated at Mayaguez, Puerto Rico during the Summer of 2010.