AAPM task group report 305: Guidance for standardization of vendor-neutral reject analysis in radiography.

Reject rate analysis is considered an integral part of a diagnostic radiography quality control (QC) program. A rejected image is a patient radiograph that was not presented to a radiologist for diagnosis and that contributes unnecessary radiation dose to the patient. Reject rates that are either too high or too low may suggest systemic department shortcomings in QC mechanisms. Due to the lack of standardization, reject data often cannot be easily compared between radiography systems from different vendors. The purpose of this report is to provide guidance to help standardize data elements that are required for comprehensive reject analysis and to propose data reporting and workflows to enable an effective and comprehensive reject rate monitoring program. Essential data elements, a proposed schema for classifying reject reasons, and workflow implementation options are recommended in this task group report.

Correction: Integrating tropical research into biology education is urgently needed.

[This corrects the article DOI: 10.1371/journal.pbio.3001674.].

Integrating tropical research into biology education is urgently needed.

Understanding tropical biology is important for solving complex problems such as climate change, biodiversity loss, and zoonotic pandemics, but biology curricula view research mostly via a temperate-zone lens. Integrating tropical research into biology education is urgently needed to tackle these issues.

Widespread homogenization of plant communities in the Anthropocene.

Native biodiversity decline and non-native species spread are major features of the Anthropocene. Both processes can drive biotic homogenization by reducing trait and phylogenetic differences in species assemblages between regions, thus diminishing the regional distinctiveness of biotas and likely have negative impacts on key ecosystem functions. However, a global assessment of this phenomenon is lacking. Here, using a dataset of >200,000 plant species, we demonstrate widespread and temporal decreases in species and phylogenetic turnover across grain sizes and spatial extents. The extent of homogenization within major biomes is pronounced and is overwhelmingly explained by non-native species naturalizations. Asia and North America are major sources of non-native species; however, the species they export tend to be phylogenetically close to recipient floras. Australia, the Pacific and Europe, in contrast, contribute fewer species to the global pool of non-natives, but represent a disproportionate amount of phylogenetic diversity. The timeline of most naturalisations coincides with widespread human migration within the last ~500 years, and demonstrates the profound influence humans exert on regional biotas beyond changes in species richness.

Machine learning predicts large scale declines in native plant phylogenetic diversity.

Though substantial effort has gone into predicting how global climate change will impact biodiversity patterns, the scarcity of taxon-specific information has hampered the efficacy of these endeavors. Further, most studies analyzing spatiotemporal patterns of biodiversity focus narrowly on species richness. We apply machine learning approaches to a comprehensive vascular plant database for the United States and generate predictive models of regional plant taxonomic and phylogenetic diversity in response to a wide range of environmental variables. We demonstrate differences in predicted patterns and potential drivers of native vs nonnative biodiversity. In particular, native phylogenetic diversity is likely to decrease over the next half century despite increases in species richness. We also identify that patterns of taxonomic diversity can be incongruent with those of phylogenetic diversity. The combination of macro-environmental factors that determine diversity likely varies at continental scales; thus, as climate change alters the combinations of these factors across the landscape, the collective effect on regional diversity will also vary. Our study represents one of the most comprehensive examinations of plant diversity patterns to date and demonstrates that our ability to predict future diversity may benefit tremendously from the application of machine learning.

Phenology models using herbarium specimens are only slightly improved by using finer-scale stages of reproduction.

PREMISE OF THE STUDY: Herbarium specimens are increasingly used to study reproductive phenology. Here, we ask whether classifying reproduction into progressively finer-scale stages improves our understanding of the relationship between climate and reproductive phenology. METHODS: We evaluated Acer rubrum herbarium specimens across eastern North America, classifying them into eight reproductive phenophases and four stages of leaf development. We fit models with different reproductive phenology categorization schemes (from detailed to broad) and compared model fits and coefficients describing temperature, elevation, and year effects. We fit similar models to leaf phenology data to compare reproductive to leafing phenology. RESULTS: Finer-scale reproductive phenophases improved model fits and provided more precise estimates of reproductive phenology. However, models with fewer reproductive phenophases led to similar qualitative conclusions, demonstrating that A. rubrum reproduces earlier in warmer locations, lower elevations, and in recent years, as well as that leafing phenology is less strongly influenced by temperature than is reproductive phenology. DISCUSSION: Our study suggests that detailed information on reproductive phenology provides a fuller understanding of potential climate change effects on flowering, fruiting, and leaf-out. However, classification schemes with fewer reproductive phenophases provided many similar insights and may be preferable in cases where resources are limited.

Patterns and predictors of fleshy fruit phenology at five international botanical gardens.

PREMISE OF THE STUDY: To improve our understanding of the patterns and drivers of fleshy fruit phenology, we examined the sequence, patterns across years and locations, and drivers of fruiting times at five botanical gardens on three continents. METHODS: We monitored four stages of fruit phenology for 406 temperate, fleshy-fruited, woody plant species in 2014 and 2015. KEY RESULTS: Across all gardens, ripe fruits were present from May to March of the following year, with peak fruiting durations ranging from under 1 week to over 150 days. Species-level first fruiting and onset of peak fruiting dates were strongly associated with one another within sites and were more consistent between years and sites than the end of peak fruiting and last fruiting date. The order of fruiting among species between years and gardens was moderately consistent, and both peak fruiting times and fruiting durations were found to be phylogenetically conserved. CONCLUSIONS: The consistent order of fruiting among species between years and locations indicates species-specific phenological responses to environmental conditions. Wide variation in fruiting times across species and in the duration of peak fruiting reinforces the importance of understanding how plant phenology impacts dispersers and monitoring the health and consistency of these interactions.

Current state of practice regarding digital radiography exposure indicators and deviation indices: Report of AAPM Imaging Physics Committee Task Group 232.

Beginning with the advent of digital radiography systems in 1981, manufacturers of these systems provided indicators of detector exposure. These indicators were manufacturer-specific, and users in facilities with equipment from multiple manufacturers found it a challenge to monitor and manage variations in indicated exposure in routine clinical use. In 2008, a common definition of exposure index (EI) was realized in International Electrotechnical Commission (IEC) International Standard 62494-1 Ed. 1, which also introduced and defined the deviation index (DI), a number quantifying the difference between the detector EI for a given radiograph and the target exposure index (EIT ). An exposure index that differed by a constant from that established by the IEC and the concept of the deviation index also appear in American Association of Physicists in Medicine (AAPM) Report No. 116 published in 2009. The AAPM Report No. 116 went beyond the IEC standard in supplying a table (Table II in the report of TG-116) titled "Exposure Indicator DI Control Limits for Clinical Images," which listed suggested DI ranges and actions to be considered for each range. As the IEC EI was implemented and clinical DI data were gathered, concerns were voiced that the DI control limits published in the report of TG-116 were too strict and did not accurately reflect clinical practice. The charge of task group 232 (TG-232) and the objective of this final report was to investigate the current state of the practice for CR/DR Exposure and Deviation Indices based on AAPM TG 116 and IEC-62494, for the purpose of establishing achievable goals (reference levels) and action levels in digital radiography. Data corresponding to EI and DI were collected from a range of practice settings for a number of body parts and views (adults and pediatric radiographs) and analyzed in aggregate and separately. A subset of radiographs was also evaluated by radiologists based on criteria adapted from the European Guidelines on Quality Criteria for Diagnostic Radiographic Images from the European Commission. Analysis revealed that typical DI distribution was characterized by a standard deviation (SD) of 1.3-3.6 with mean DI values substantially different from 0.0, and less than 50% of DI values fell within the significant action limits proposed by AAPM TG-116 (-1.0 ≤ DI ≤ 1.0). Recommendations stemming from this analysis include targeting a mean DI value of 0.0 and action limits at ±1 and ±2 SD of the DI based on actual DI data of an individual site. EIT values, DI values, and associated action limits should be reviewed on an ongoing basis and optimization of DI values should be a process of continuous quality improvement with a goal of reducing practice variation.

Medical physics 3.0 versus 1.0: A case study in digital radiography quality control.

PURPOSE: The study illustrates how a renewed approach to medical physics, Medical Physics 3.0 (MP3.0), can identify performance decrement of digital radiography (DR) systems when conventional Medical Physics 1.0 (MP1.0) methods fail. METHODS: MP1.0 tests included traditional annual tests plus the manufacturer's automated Quality Assurance Procedures (QAP) of a DR system before and after a radiologist's image quality (IQ) complaint repeated after service intervention. Further analysis was conducted using nontraditional MP3.0 tests including longitudinal review of QAP results from a 15-yr database, exposure-dependent signal-to-noise (SNR2 ), clinical IQ, and correlation with the institutional service database. Clinical images were analyzed in terms of IQ metrics by the Duke University Clinical Imaging Physics Group using previously validated software. RESULTS: Traditional metrics did not indicate discrepant system performance at any time. QAP reported a decrease in contrast-to-noise ratio (CNR) after detector replacement, but remained above the manufacturer's action limit. Clinical images showed increased lung noise (Ln), mediastinum noise (Mn), and subdiaphragm-lung contrast (SLc), and decreased lung gray level (Lgl) following detector replacement. After detector recalibration, QAP CNR improved, but did not return to previous levels. Lgl and SLc no longer significantly differed from before detector recalibration; however, Ln and Mn remained significantly different. Exposure-dependent SNR2 documented the detector operating within acceptable limits 9 yr previously but subsequently becoming miscalibrated sometime before four prior annual tests. Service records revealed catastrophic failure of the computer containing the original detector calibration from 11 yr prior. It is likely that the incorrect calibration backup file was uploaded at that time. CONCLUSIONS: MP1.0 tests failed to detect substandard system performance, but MP3.0 methods determined the root cause of the problem. MP3.0 exploits the wealth of data with more sensitive performance indicators. Data analytics are powerful tools whose proper application could facilitate early intervention in degraded system performance.

Automated quality control assessment of clinical chest images.

PURPOSE: The purpose of this study was to determine whether a proposed suite of objective image quality metrics for digital chest radiographs is useful for monitoring image quality in a clinical setting unique from the one where the metrics were developed. METHODS: Seventeen gridless AP chest radiographs from a GE Optima portable digital radiography (DR) unit ("sub-standard" images; Group 2) and 17 digital PA chest radiographs ("standard-of-care" images; Group 1) and 15 gridless (non-routine) PA chest radiographs (images with a gross technical error; Group 3) from a Discovery DR unit were chosen for analysis. Group 2 images were acquired with a lower kVp (100 vs 125) and shorter source-to-image distance (127 cm vs 183 cm) and were expected to have lower quality than Group 1 images. Group 3 images were expected to have degraded contrast vs Group 1 images. Images were anonymized and securely transferred to the Duke University Clinical Imaging Physics Group for analysis using software described and validated previously. Individual image quality was reported in terms of lung gray level, lung detail, lung noise, rib-lung contrast, rib sharpness, mediastinum detail, mediastinum noise, mediastinum alignment, subdiaphragm-lung contrast, and subdiaphragm area. Metrics were compared across groups. To improve precision of means and confidence intervals for routine exams, an additional 66 PA images were acquired, processed, and pooled with Group 1. Three observer studies were conducted to assess whether humans were able to identify images classified by the algorithm as abnormal. RESULTS: Metrics agreed with published Quality Consistency Ranges with three exceptions: higher lung gray level, lower rib-lung contrast, and lower subdiaphragm-lung contrast. Higher (stored) bit depth (14 vs 12) accounted for higher lung gray level values in our images. Values were most internally consistent for Group 1. The most sensitive metric for distinguishing between groups was mediastinum noise, followed closely by lung noise. The least sensitive metrics were mediastinum detail and rib-lung contrast. The algorithm was more sensitive than human observers at detecting suboptimal diagnostic quality images. CONCLUSIONS: The software appears promising for objectively and automatically identifying suboptimal images in a clinical imaging operation. The results can be used to establish local quality consistency ranges and action limits per facility preferences.

Digitization protocol for scoring reproductive phenology from herbarium specimens of seed plants.

PREMISE OF THE STUDY: Herbarium specimens provide a robust record of historical plant phenology (the timing of seasonal events such as flowering or fruiting). However, the difficulty of aggregating phenological data from specimens arises from a lack of standardized scoring methods and definitions for phenological states across the collections community. METHODS AND RESULTS: To address this problem, we report on a consensus reached by an iDigBio working group of curators, researchers, and data standards experts regarding an efficient scoring protocol and a data-sharing protocol for reproductive traits available from herbarium specimens of seed plants. The phenological data sets generated can be shared via Darwin Core Archives using the Extended MeasurementOrFact extension. CONCLUSIONS: Our hope is that curators and others interested in collecting phenological trait data from specimens will use the recommendations presented here in current and future scoring efforts. New tools for scoring specimens are reviewed.

Herbarium specimens show patterns of fruiting phenology in native and invasive plant species across New England.

PREMISE OF THE STUDY: Patterns of fruiting phenology in temperate ecosystems are poorly understood, despite the ecological importance of fruiting for animal nutrition and seed dispersal. Herbarium specimens represent an under-utilized resource for investigating geographical and climatic factors affecting fruiting times within species, patterns in fruiting times among species, and differences between native and non-native invasive species. METHODS: We examined over 15,000 herbarium specimens, collected and housed across New England, and found 3159 specimens with ripe fruits, collected from 1849-2013. We examined patterns in fruiting phenology among 37 native and 18 invasive woody plant species common to New England. We compared fruiting dates between native and invasive species, and analyzed how fruiting phenology varies with temperature, space, and time. KEY RESULTS: Spring temperature and year explained a small but significant amount of the variation in fruiting dates. Accounting for the moderate phylogenetic signal in fruiting phenology, invasive species fruited 26 days later on average than native species, with significantly greater standard deviations. CONCLUSIONS: Herbarium specimens can be used to detect patterns in fruiting times among species. However, the amount of intraspecific variation in fruiting times explained by temporal, geographic, and climatic predictors is small, due to a combination of low temporal resolution of fruiting specimens and the protracted nature of fruiting. Later fruiting times in invasive species, combined with delays in autumn bird migrations in New England, may increase the likelihood that migratory birds will consume and disperse invasive seeds in New England later into the year.

Widespread sampling biases in herbaria revealed from large-scale digitization.

Nonrandom collecting practices may bias conclusions drawn from analyses of herbarium records. Recent efforts to fully digitize and mobilize regional floras online offer a timely opportunity to assess commonalities and differences in herbarium sampling biases. We determined spatial, temporal, trait, phylogenetic, and collector biases in c. 5 million herbarium records, representing three of the most complete digitized floras of the world: Australia (AU), South Africa (SA), and New England, USA (NE). We identified numerous shared and unique biases among these regions. Shared biases included specimens collected close to roads and herbaria; specimens collected more frequently during biological spring and summer; specimens of threatened species collected less frequently; and specimens of close relatives collected in similar numbers. Regional differences included overrepresentation of graminoids in SA and AU and of annuals in AU; and peak collection during the 1910s in NE, 1980s in SA, and 1990s in AU. Finally, in all regions, a disproportionately large percentage of specimens were collected by very few individuals. We hypothesize that these mega-collectors, with their associated preferences and idiosyncrasies, shaped patterns of collection bias via 'founder effects'. Studies using herbarium collections should account for sampling biases, and future collecting efforts should avoid compounding these biases to the extent possible.

Phylogenetic conservatism and trait correlates of spring phenological responses to climate change in northeast China.

Climate change has resulted in major changes in plant phenology across the globe that includes leaf-out date and flowering time. The ability of species to respond to climate change, in part, depends on their response to climate as a phenological cue in general. Species that are not phenologically responsive may suffer in the face of continued climate change. Comparative studies of phenology have found phylogeny to be a reliable predictor of mean leaf-out date and flowering time at both the local and global scales. This is less true for flowering time response (i.e., the correlation between phenological timing and climate factors), while no study to date has explored whether the response of leaf-out date to climate factors exhibits phylogenetic signal. We used a 52-year observational phenological dataset for 52 woody species from the Forest Botanical Garden of Heilongjiang Province, China, to test phylogenetic signal in leaf-out date and flowering time, as well as, the response of these two phenological traits to both temperature and winter precipitation. Leaf-out date and flowering time were significantly responsive to temperature for most species, advancing, on average, 3.11 and 2.87 day/°C, respectively. Both leaf-out and flowering, and their responses to temperature exhibited significant phylogenetic signals. The response of leaf-out date to precipitation exhibited no phylogenetic signal, while flowering time response to precipitation did. Native species tended to have a weaker flowering response to temperature than non-native species. Earlier leaf-out species tended to have a greater response to winter precipitation. This study is the first to assess phylogenetic signal of leaf-out response to climate change, which suggests, that climate change has the potential to shape the plant communities, not only through flowering sensitivity, but also through leaf-out sensitivity.

Old Plants, New Tricks: Phenological Research Using Herbarium Specimens.

The timing of phenological events, such as leaf-out and flowering, strongly influence plant success and their study is vital to understanding how plants will respond to climate change. Phenological research, however, is often limited by the temporal, geographic, or phylogenetic scope of available data. Hundreds of millions of plant specimens in herbaria worldwide offer a potential solution to this problem, especially as digitization efforts drastically improve access to collections. Herbarium specimens represent snapshots of phenological events and have been reliably used to characterize phenological responses to climate. We review the current state of herbarium-based phenological research, identify potential biases and limitations in the collection, digitization, and interpretation of specimen data, and discuss future opportunities for phenological investigations using herbarium specimens.

CrowdCurio: an online crowdsourcing platform to facilitate climate change studies using herbarium specimens.

Phenology is a key aspect of plant success. Recent research has demonstrated that herbarium specimens can provide important information on plant phenology. Massive digitization efforts have the potential to greatly expand herbarium-based phenological research, but also pose a serious challenge regarding efficient data collection. Here, we introduce CrowdCurio, a crowdsourcing tool for the collection of phenological data from herbarium specimens. We test its utility by having workers collect phenological data (number of flower buds, open flowers and fruits) from specimens of two common New England (USA) species: Chelidonium majus and Vaccinium angustifolium. We assess the reliability of using nonexpert workers (i.e. Amazon Mechanical Turk) against expert workers. We also use these data to estimate the phenological sensitivity to temperature for both species across multiple phenophases. We found no difference in the data quality of nonexperts and experts. Nonexperts, however, were a more efficient way of collecting more data at lower cost. We also found that phenological sensitivity varied across both species and phenophases. Our study demonstrates the utility of CrowdCurio as a crowdsourcing tool for the collection of phenological data from herbarium specimens. Furthermore, our results highlight the insight gained from collecting large amounts of phenological data to estimate multiple phenophases.

Global biogeography of seed dormancy is determined by seasonality and seed size: a case study in the legumes.

Seed dormancy is expected to provide ecological advantages by adjusting germination to the favorable growth period. However, many species produce nondormant seeds, particularly in wet tropical forests, a biogeographic pattern that is not well accounted for in current models. We hypothesized that the global distribution of dormant seeds derives from their adaptive value in predictably fluctuating (i.e. seasonal) environments. However, the advantage conferred by dormancy might ultimately depend on other seed attributes, particularly size. This general model was tested within a phylogenetically informed framework using a data set comprising > 216 000 world-wide observations of Fabaceae, spanning three orders of magnitude in seed size and including both dormant and nondormant seeds. Our results confirmed our hypothesis: nondormant seeds can only evolve in climates with long growing seasons and/or in lineages that produce larger seeds. Conversely, dormancy should be evolutionarily stable in temperate lineages with small seeds. When the favorable season is fleeting, seed dormancy is the only adaptive strategy. Based on these results, we predict that, within a given lineage, taxa producing larger, nondormant seeds will necessarily predominate in aseasonal environments, while plants bearing small, dormant seeds will be dominant under short growing seasons.

Evaluation of cassette-based digital radiography detectors using standardized image quality metrics: AAPM TG-150 Draft Image Detector Tests.

The purpose of this study was to evaluate several of the standardized image quality metrics proposed by the American Association of Physics in Medicine (AAPM) Task Group 150. The task group suggested region-of-interest (ROI)-based techniques to measure nonuniformity, minimum signal-to-noise ratio (SNR), number of anomalous pixels, and modulation transfer function (MTF). This study evaluated the effects of ROI size and layout on the image metrics by using four different ROI sets, assessed result uncertainty by repeating measurements, and compared results with two commercially available quality control tools, namely the Carestream DIRECTVIEW Total Quality Tool (TQT) and the GE Healthcare Quality Assurance Process (QAP). Seven Carestream DRX-1C (CsI) detectors on mobile DR systems and four GE FlashPad detectors in radiographic rooms were tested. Images were analyzed using MATLAB software that had been previously validated and reported. Our values for signal and SNR nonuniformity and MTF agree with values published by other investigators. Our results show that ROI size affects nonuniformity and minimum SNR measurements, but not detection of anomalous pixels. Exposure geometry affects all tested image metrics except for the MTF. TG-150 metrics in general agree with the TQT, but agree with the QAP only for local and global signal nonuniformity. The difference in SNR nonuniformity and MTF values between the TG-150 and QAP may be explained by differences in the calculation of noise and acquisition beam quality, respectively. TG-150's SNR nonuniformity metrics are also more sensitive to detector nonuniformity compared to the QAP. Our results suggest that fixed ROI size should be used for consistency because nonuniformity metrics depend on ROI size. Ideally, detector tests should be performed at the exact calibration position. If not feasible, a baseline should be established from the mean of several repeated measurements. Our study indicates that the TG-150 tests can be used as an independent standardized procedure for detector performance assessment.

Genome Sequences of Gordonia Bacteriophages Obliviate, UmaThurman, and Guacamole.

We describe three newly isolated phages-Obliviate, UmaThurman, and Guacamole-that infect Gordonia terrae 3612. The three genomes are related to one another but are not closely related to other previously sequenced phages or prophages. The three phages are predicted to use integration-dependent immunity systems as described in several mycobacteriophages.

Herbarium records are reliable sources of phenological change driven by climate and provide novel insights into species' phenological cueing mechanisms.

PREMISE OF THE STUDY: Climate change has resulted in major changes in the phenology of some species but not others. Long-term field observational records provide the best assessment of these changes, but geographic and taxonomic biases limit their utility. Plant specimens in herbaria have been hypothesized to provide a wealth of additional data for studying phenological responses to climatic change. However, no study to our knowledge has comprehensively addressed whether herbarium data are accurate measures of phenological response and thus applicable to addressing such questions. METHODS: We compared flowering phenology determined from field observations (years 1852-1858, 1875, 1878-1908, 2003-2006, 2011-2013) and herbarium records (1852-2013) of 20 species from New England, United States. KEY RESULTS: Earliest flowering date estimated from herbarium records faithfully reflected field observations of first flowering date and substantially increased the sampling range across climatic conditions. Additionally, although most species demonstrated a response to interannual temperature variation, long-term temporal changes in phenological response were not detectable. CONCLUSIONS: Our findings support the use of herbarium records for understanding plant phenological responses to changes in temperature, and also importantly establish a new use of herbarium collections: inferring primary phenological cueing mechanisms of individual species (e.g., temperature, winter chilling, photoperiod). These latter data are lacking from most investigations of phenological change, but are vital for understanding differential responses of individual species to ongoing climate change.