Resequencing: The Untold Story - Recognizing False Positives, False Negatives and Structural Variation in User Data
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99 GENERAL AND MISCELLANEOUS↗
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Predictive analytics of selections of russet potatoes
We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.
60 APPLIED LIFE SCIENCES↗
SU-F-T-235: Optical Scan Based Collision Avoidance Using Multiple Stereotactic Cameras During Simulation
Purpose: To demonstrate the ability to quickly generate an accurate collision avoidance map using multiple stereotactic cameras during simulation. Methods: Three Kinect stereotactic cameras were placed in the CT simulation room and optically calibrated to the DICOM isocenter. Immediately before scanning, the patient was optically imaged to generate a 3D polygon mesh, which was used to calculate the collision avoidance area using our previously developed framework. The mesh was visually compared to the CT scan body contour to ensure accurate coordinate alignment. To test the accuracy of the collision calculation, the patient and machine were physically maneuvered in the treatment room to calculated collision boundaries. Results: The optical scan and collision calculation took 38.0 seconds and 2.5 seconds to complete respectively. The collision prediction accuracy was determined using a receiver operating curve (ROC) analysis, where the true positive, true negative, false positive and false negative values were 837, 821, 43, and 79 points respectively. The ROC accuracy was 93.1% over the sampled collision space. Conclusion: We have demonstrated a framework which is fast and accurate for predicting collision avoidance for treatment which can be determined during the normal simulation process. Because of the speed, the system could be used to add a layer of safety with a negligible impact on the normal patient simulation experience. This information could be used during treatment planning to explore the feasible geometries when optimizing plans. Research supported by Varian Medical Systems.
60 APPLIED LIFE SCIENCES↗
Overview of Deployed EDS Technologies And Third Party Involvement with Advancedments
The term explosive detection system (EDS) is used by the TSA to describe equipment that is certified to detect explosives in checked bags. The EDS, as certified, by the TSL must consist of device for interrogating a bag and an automated detection algorithm (ATD) for evaluating the results of the interrogation. We only consider CT as the interrogation device in this report. A schematic drawing of a CT-based EDS is shown in Figure 2. The output of the ATD is the binary decision of alarm or non-alarm. Alarms may true- or false-positives. Non-alarms may be true- or false-negatives. False positives are also denoted false alarms. The true detection means that the ATD reports an alarm when a threat is present in the scanned bag. The probability of detecting a threat given that a threat is present is denoted the probability of detection (PD). The probability of false alarm (PFA) is the case when an alarm is reported when a threat is not present in a bag. Certification in this context means passing tests for PD and PFA at the TSL. The results of the EDS include CT cross-sectional images of the bag and specifics about the alarmed objects generated by ATD. These results are presented on a display so that a person may override the decision of ATD and declare the alarm to be a non-alarm. This process is denoted clearing. Bags that are not cleared by the person are sent to a secondary inspection process. Here the bags may be opened or assessed with explosive trace detection (ETD) in order to clear the bags. Bags that are not cleared at this point are evaluated by an ordinance disposal team. The CT scanner along with ATD is denoted Level 1 screening. The process of clearing on a display is denoted Level 2 screening. Secondary inspection is denoted Level 3 screening. Vendors of the deployed EDSs supply the TSA with equipment for all three levels. Therefore, the term EDS may include the equipment provided for Levels 1, 2 and 3. A schematic diagram of an EDS and the levels of screening are shown in Figure 7. The decision processes used as a bag is scanned and cleared is shown in Figure 8. Shield alarms and exceptions are discussed below. Since most alarms are false alarms, the probability of alarm is oftentimes denoted the probability of a false alarm. The expenses associated with clearing false alarms occur in Levels 2 and 3.
42 ENGINEERING↗
Validation of Atmosphere/Ionosphere Signals Associated with Major Earthquakes by Multi-Instrument Space-Borne and Ground Observations
The latest catastrophic earthquake in Japan (March 2011) has renewed interest in the important question of the existence of pre-earthquake anomalous signals related to strong earthquakes. Recent studies have shown that there were precursory atmospheric/ionospheric signals observed in space associated with major earthquakes. The critical question, still widely debated in the scientific community, is whether such ionospheric/atmospheric signals systematically precede large earthquakes. To address this problem we have started to investigate anomalous ionospheric / atmospheric signals occurring prior to large earthquakes. We are studying the Earth's atmospheric electromagnetic environment by developing a multisensor model for monitoring the signals related to active tectonic faulting and earthquake processes. The integrated satellite and terrestrial framework (ISTF) is our method for validation and is based on a joint analysis of several physical and environmental parameters (thermal infrared radiation, electron concentration in the ionosphere, lineament analysis, radon/ion activities, air temperature and seismicity) that were found to be associated with earthquakes. A physical link between these parameters and earthquake processes has been provided by the recent version of Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) model. Our experimental measurements have supported the new theoretical estimates of LAIC hypothesis for an increase in the surface latent heat flux, integrated variability of outgoing long wave radiation (OLR) and anomalous variations of the total electron content (TEC) registered over the epicenters. Some of the major earthquakes are accompanied by an intensification of gas migration to the surface, thermodynamic and hydrodynamic processes of transformation of latent heat into thermal energy and with vertical transport of charged aerosols in the lower atmosphere. These processes lead to the generation of external electric currents in specific regions of the atmosphere and the modifications, by dc electric fields, in the ionosphere-atmosphere electric circuit. We retrospectively analyzed temporal and spatial variations of four different physical parameters (gas/radon counting rate, lineaments change, long-wave radiation transitions and ionospheric electron density/plasma variations) characterizing the state of the lithosphere/atmosphere coupling several days before the onset of the earthquakes. Validation processes consist in two phases: A. Case studies for seven recent major earthquakes: Japan (M9.0, 2011), China (M7.9, 2008), Italy (M6.3, 2009), Samoa (M7, 2009), Haiti (M7.0, 2010) and, Chile (M8.8, 2010) and B. A continuous retrospective analysis was preformed over two different regions with high seismicity- Taiwan and Japan for 2003-2009. Satellite, ground surface, and troposphere data were obtained from Terra/ASTER, Aqua/AIRS, POES and ionospheric variations from DEMETER and COSMIC-I data. Radon and GPS/TEC were obtaining from monitoring sites in Taiwan, Japan and Italy and from global ionosphere maps (GIM) respectively. Our analysis of ground and satellite data during the occurrence of 7 global earthquakes has shown the presence of anomalies in the atmosphere. Our results for Tohoku M9.0 earthquake show that on March 7th, 2011 (4 days before the main shock and 1 day before the M7.2 foreshock of March 8, 2011) a rapid increase of emitted infrared radiation was observed by the satellite data and an anomaly was developed near the epicenter. The GPS/TEC data indicate an increase and variation in electron density reaching a maximum value on March 8. From March 3 to 11 a large increase in electron concentration was recorded at all four Japanese ground-based ionosondes, which returned to normal after the main earthquake. Similar approach for analyzing atmospheric and ionospheric parameters has been applied for China (M7.9, 2008), Italy (M6.3, 2009), Samoa (M7, 2009), Haiti (M7.0, 2010) and Chile (M8.8, 2010) eahquakes. Results have revealed the presence of related variations of these parameters implying their connection with the earthquake process. The second phase (B) of this validation included 102 major earthquakes (M>5.9) in Taiwan and Japan. We have found anomalous behavior before all of these events with no false negatives. False alarm ratio for false positives is less then 10% and has been calculated for the same month of the earthquake occurrence for the entire period of analysis (2003-2009). The commonalities for detecting atmospheric/ionospheric anomalies are: i.) Regularly appearance over regions of maximum stress (i.e., along plate boundaries); ii.) Anomaly existence over land and sea; and iii) association with M>5.9 earthquakes not deeper than 100km. Due to their long duration over the same region these anomalies are not consistent with a meteorological origin. Our initial results from the ISTF validation of multi-instrument space-borne and ground observations show a systematic appearance of atmospheric anomalies near the epicentral area, one to seven (average) days prior to the largest earthquakes, and suggest that it could be explained by a coupling process between the observed physical parameters and the pre-earthquake preparation processes.
Ouzounov, Dimitar↗
Multi-Sensor Observations of Earthquake Related Atmospheric Signals over Major Geohazard Validation Sites
We are conducting a scientific validation study involving multi-sensor observations in our investigation of phenomena preceding major earthquakes. Our approach is based on a systematic analysis of several atmospheric and environmental parameters, which we found, are associated with the earthquakes, namely: thermal infrared radiation, outgoing long-wavelength radiation, ionospheric electron density, and atmospheric temperature and humidity. For first time we applied this approach to selected GEOSS sites prone to earthquakes or volcanoes. This provides a new opportunity to cross validate our results with the dense networks of in-situ and space measurements. We investigated two different seismic aspects, first the sites with recent large earthquakes, viz.- Tohoku-oki (M9, 2011, Japan) and Emilia region (M5.9, 2012,N. Italy). Our retrospective analysis of satellite data has shown the presence of anomalies in the atmosphere. Second, we did a retrospective analysis to check the re-occurrence of similar anomalous behavior in atmosphere/ionosphere over three regions with distinct geological settings and high seismicity: Taiwan, Japan and Kamchatka, which include 40 major earthquakes (M>5.9) for the period of 2005-2009. We found anomalous behavior before all of these events with no false negatives; false positives were less then 10%. Our initial results suggest that multi-instrument space-borne and ground observations show a systematic appearance of atmospheric anomalies near the epicentral area that could be explained by a coupling between the observed physical parameters and earthquake preparation processes.
Ouzounov, D.↗
TPSAS-NF1676L-32111-DND
Automated in-situ NDE monitoring of component state during flight or at set increments on ground often requires integrated instrumentation. Challenges: false negatives, false positives; system validation/certification (confidence in detectability, assessment of limitations, and assessment of lifetime).
Elizabeth Gregory↗
HVAC control fine-grained occupancy pattern estimation
A thermal state of a plurality of zones of the building is updated according to a building thermal model and information received from temperature sensors of the building. Predicted occupant counts for an upcoming plurality of time slots for each of the plurality of zones are updated using actual occupancy counts for each of the plurality of zones. A misprediction type distribution for the upcoming plurality of time slots for each of the plurality of zones is updated, the misprediction type distribution indicating misprediction for true negatives, false positives, false negatives, and true positives. A total misprediction cost expectation is updated according to the predicted occupant counts and the misprediction type distribution. HVAC power for each of the plurality of zones is determined to optimize occupant thermal comfort weighted according to the predicted occupant counts while minimizing the total misprediction cost expectation. HVAC operation is controlled per the HVAC power.
Lin, Shan↗
Coastal Acoustic Buoy for Offshore Wind: Project Synthesis
The population of North Atlantic right whales is critically endangered and their habitat overlaps with offshore windfarm leases. It is therefore imperative that effective mitigation strategies be used to avoid impacts on right whales during the construction of offshore windfarms. The Department of Energy issued FOA Number DE-FOA-0001924 to encourage the development of technology that could monitor large exclusion zones for right whales in order to mitigate potential impact of construction noise on right whales. This report summarizes the past two years of the development and evaluation of the Coastal Acoustic Buoy for Offshore Wind (CABOW) project which aimed to develop technology to monitor large exclusion zones for North Atlantic right whales. Over the course of the project SMRU Consulting have implemented a rigorous design process including comparison of different approaches (e.g., single sensor vs multiple sensors), as well as consideration of placement and timing of acoustic monitoring. We have evaluated critical components of the CABOW system including, reliability, detection range, and bearing accuracy in areas adjacent to offshore windfarm leases in Maryland by conducting 3,536 playbacks of simulated right whale upcalls. The maximum call detection range was 7.5 km when noise was 99 dB re 1µPa rms (50-225 Hz), but this reduced to < 1 km when ambient noise levels were high. Our detection probability in the field was measured as a function of range as well as the source-to-noise level ratio allowing us to build a model to predict the probability of detection under various scenarios (sample size: 3,536 calls x 5 buoys = 17,680). The median bearing error was -0.25° but this is likely an underestimate of error due to experimental design. Using the published recall and precision of the two detectors we implemented in the CABOW system, we estimate that at a recall of 80%, our precision was > 80%, within the range of what we were aiming for in this project. To estimate our exclusion zone false negative and false positive rates, we built a simulation model using the empirical data from our field trial. We modelled three to nine CABOW units placed on the 10 km exclusion zone and estimated our false negative rate to be 1% or less (which was our project goal) and our false positive rate to be between seven and nine percent, slightly above our goal of 5%. However, we also modelled an equivalent PAM system that does not have bearing capabilities and found the false positive rates for that system to be six to eight times higher than the CABOW rate. This higher false positive rate of PAM systems with low spatial information could have significant cost repercussions for offshore wind developers by adding work shutdowns or delays without providing additional protection for right whales. The model we built allows us to explore the placement of PAM systems under various scenarios and will thus help facilitate planning of PAM mitigation systems to meet NOAA Incidental Harassment Authorizations for specific windfarms. We achieved an average system uptime of 98.3%, just below our goal of 99%. The issues that caused these short losses of data have been identified and fixed. Right whale detections and audio clips were typically transferred via radio from the buoys to the base station in two to four seconds. We therefore believe we have developed a highly robust real-time PAM system. Based on the above, we feel we have achieved the stated funding goal of developing a cost-effective and robust real-time PAM system that enables the monitoring of large exclusion zones for right whales during the constructions of offshore windfarms. This should lead to decreased costs and risks for the offshore wind sector while providing robust mitigation for right whales. It is important to state that PAM mitigation will need to be implemented with other mitigation strategies (e.g., visual observers) to provide a complete mitigation strategy to ensure that any effects on right whales from offshore windfarm construction is minimized.
17 WIND ENERGY↗
Bayesian event categorization matrix approach for explosion monitoring
Current efforts to correctly categorize natural events from suspected explosion sources with data that is collected by ground- or space-based sensors presents historical challenges that remain unaddressed by the Event Categorization Matrix (ECM) model. Smaller historical events (lower yield explosions) may have data available from fewer measurement techniques than are available today, and therefore, a historical event record can lack a complete set of discriminants. The covariance structures can also differ between such observations of event (source-type) categories. Both obstacles are problematic for the classic ECM model. Our work addresses this gap and presents a Bayesian update to the previous ECM model, termed the Bayesian Event Categorization Matrix model, which can be trained on partial observations and does not rely on a pooled covariance structure. We further augment the ECM model with Bayesian Decision Theory so that false negative or false positive rates of an event categorization can be reduced in an intuitive manner. To demonstrate improved categorization rates for the Bayesian Event Categorization Matrix model, we compare an array of Bayesian and classic models with multiple performance metrics using Monte Carlo experiments. We use both synthetic and real data. Our Bayesian models show consistent gains in overall accuracy and lower false negative rates relative to the classic ECM model. Here, we propose future avenues to improve Bayesian Event Categorization Matrix models’ decision making and predictive capability.
58 GEOSCIENCES↗
The effect of abnormal cell proportion on specimen classifier performance
An analysis is presented of the results obtained from a cell classifier which is confronted with an abnormal/normal cell ratio which is different from the ratio assumed in the calibration of the classifier. False negative and false positive error rates are determined in advance for classifier operation, along with the necessary sample size in order to validate the predicted distributions. Changes are demonstrated to happen only regarding the false negative rate, where reductions in the abnormal cell rate below the expected rates would cause totally unreliable data. Substantial overproduction of abnormal cells would be quickly noticeable, while production rates beyond, but close to, the expected rates would only require more extensive sampling. Classifier systems for 10% proportions of abnormal cells are concluded to be possible, but difficulties are present with much lower rates
Castleman, K. R.↗
A robust benchmark for detection of germline large deletions and insertions
New technologies and analysis methods are enabling genomic structural variants (SVs) to be detected with ever-increasing accuracy, resolution and comprehensiveness. To help translate these methods to routine research and clinical practice, we developed a sequence-resolved benchmark set for identification of both false-negative and false-positive germline large insertions and deletions. To create this benchmark for a broadly consented son in a Personal Genome Project trio with broadly available cells and DNA, the Genome in a Bottle Consortium integrated 19 sequence-resolved variant calling methods from diverse technologies. The final benchmark set contains 12,745 isolated, sequence-resolved insertion (7,281) and deletion (5,464) calls ≥50 base pairs (bp). The Tier 1 benchmark regions, for which any extra calls are putative false positives, cover 2.51 Gbp and 5,262 insertions and 4,095 deletions supported by ≥1 diploid assembly. We demonstrate that the benchmark set reliably identifies false negatives and false positives in high-quality SV callsets from short-, linked- and long-read sequencing and optical mapping.
59 BASIC BIOLOGICAL SCIENCES↗
Dual-time-point FDG PET/CT imaging in prosthetic heart valve endocarditis
Purpose: FDG PET/CT has been of increasing interest in the diagnostic workup of prosthetic heart valve endocarditis (PVE). Some reports advocate later imaging time points to improve the diagnostic accuracy for PVE. In this study, we compared standard and late FDG PET/CT images in patients with a clinical suspicion of PVE. Materials and Methods: Fourteen scans in 13 patients referred for FDG PET/CT for suspicion of PVE performed at standard (60 min post injection) and late (150 min post injection) time points were scored based on visual interpretation and semi-quantitatively with SUVmax and target-to-background ratio (TBR, defined as [SUVmax valve/SUVmean blood pool]). Final diagnosis was based on surgical findings in all cases of infection (n = 6) and unremarkable follow-up in all others (n = 8). Results: Late images were more prone to false positive interpretation for both visual and semi-quantitative analyses. Visual analysis of the standard images yielded 1 false negative and 1 false positive result. On the late images, no scans were false negative but 5 scans were false positive. Conclusion: Late FDG PET/CT imaging for PVE seems prone to false positive results. Therefore, late imaging should be interpreted with caution.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Novel Human Radiation Exposure Biomarker Panel Applicable for Population Triage
Purpose: To identify a panel of radiation-responsive plasma proteins that could be used in a point-of-care biologic dosimeter to detect clinically significant levels of ionizing radiation exposure. Methods and Materials: Patients undergoing preparation for hematopoietic cell transplantation using radiation therapy (RT) with either total lymphoid irradiation or fractionated total body irradiation were eligible. Plasma was examined from patients with potentially confounding conditions and from normal individuals. Each plasma sample was analyzed for a panel of 17 proteins before RT was begun and at several time points after RT exposure. Paired and unpaired t tests between the dose and control groups were performed. Conditional inference trees were constructed based on panels of proteins to compare the non-RT group with the RT group. Results: A total of 151 patients (62 RT, 41 infection, 48 trauma) were enrolled on the study, and the plasma from an additional 24 healthy control individuals was analyzed. In comparison with to control individuals, tenascin-C was upregulated and clusterin was downregulated in patients receiving RT. Salivary amylase was strongly radiation responsive, with upregulation in total body irradiation patients and slight downregulation in total lymphoid irradiation patients compared with control individuals. A panel consisting of these 3 proteins accurately distinguished between irradiated patients and healthy control individuals within 3 days after exposure: 97% accuracy, 0.5% false negative rate, 2% false positive rate. The accuracy was diminished when patients with trauma, infection, or both were included (accuracy, 74%-84%; false positive rate, 14%-33%, false negative rate: 8%-40%). Conclusions: A panel of 3 proteins accurately distinguishes unirradiated healthy donors from those exposed to RT (0.8-9.6 Gy) within 3 days of exposure. These findings have significant implications in terms of triaging individuals in the case of nuclear or other radiologic events.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Current nonclinical testing paradigm enables safe entry to First-In-Human clinical trials: The IQ consortium nonclinical to clinical translational database
The contribution of animal testing in drug development has been widely debated and challenged. An industry-wide nonclinical to clinical translational database was created to determine how safety assessments in animal models translate to First-In-Human clinical risk. The blinded database was composed of 182 molecules and contained animal toxicology data coupled with clinical observations from phase I human studies. Animal and clinical data were categorized by organ system and correlations determined. The 2 × 2 contingency table (true positive, false positive, true negative, false negative) was used for statistical analysis. Sensitivity was 48% with a 43% positive predictive value (PPV). The nonhuman primate had the strongest performance in predicting adverse effects, especially for gastrointestinal and nervous system categories. When the same target organ was identified in both the rodent and nonrodent, the PPV increased. Specificity was 84% with an 86% negative predictive value (NPV). The beagle dog had the strongest performance in predicting an absence of clinical adverse effects. If no target organ toxicity was observed in either test species, the NPV increased. While nonclinical studies can demonstrate great value in the PPV for certain species and organ categories, the NPV was the stronger predictive performance measure across test species and target organs indicating that an absence of toxicity in animal studies strongly predicts a similar outcome in the clinic. These results support the current regulatory paradigm of animal testing in supporting safe entry to clinical trials and provide context for emerging alternate models. - Highlights: • Translational database created to determine animal to human safety concordance. • 182 molecules with animal and clinical data; concordance parameters determined • Positive predictive value of 43%; Negative predictive value of 86% • Absence of toxicity in animal study strongly predicts similar outcome in the clinic. • Database supports animal testing to ensure human safety in the clinic.
60 APPLIED LIFE SCIENCES↗
Trust Model System for the Energy Grid of Things Network Communications
Network communication is crucial in the Energy Grid of Things (EGoT). Without a network connection, the energy grid becomes just a power grid where the energy resources are available to the customer uni-directionally. A mechanism to analyze and optimize the energy usage of the grid can only happen through a medium, a communications network, that enables information exchange between the grid participants and the service provider. Security implementers of EGoT network communication take extraordinary measures to ensure the safety of the energy grid, a critical infrastructure, as well as the safety and privacy of the grid participants. With the dynamic nature of network communication of the EGoT, the information provided by the customer or the service provider can be falsified by a malicious attacker. Therefore, a trust model is necessary to monitor any abnormal activities. This paper describes a distributed trust model system that meets the need of the EGoT. This paper describes methods for evaluating and improving the distributed trust model using standard hypothesis testing metrics such as true positive, false positive, true negative, false negative, equal error rate, and F1 score. Example calculations are shown based on generated sample data.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Identifying preferential flow from soil moisture time series: Review of methodologies
Abstract Identifying and quantifying preferential flow (PF) through soil—the rapid movement of water through spatially distinct pathways in the subsurface—is vital to understanding how the hydrologic cycle responds to climate, land cover, and anthropogenic changes. In recent decades, methods have been developed that use measured soil moisture time series to identify PF. Because they allow for continuous monitoring and are relatively easy to implement, these methods have become an important tool for recognizing when, where, and under what conditions PF occurs. The methods seek to identify a pattern or quantification that indicates the occurrence of PF. Most commonly, the chosen signature is either (1) a nonsequential response to infiltrated water, in which soil moisture responses do not occur in order of shallowest to deepest, or (2) a velocity criterion, in which newly infiltrated water is detected at depth earlier than is possible by nonpreferential flow processes. Alternative signatures have also been developed that have certain advantages but are less commonly utilized. Choosing among these possible signatures requires attention to their pertinent characteristics, including susceptibility to errors, possible bias toward false negatives or false positives, reliance on subjective judgments, and possible requirements for additional types of data. We review 77 studies that have applied such methods to highlight important information for readers who want to identify PF from soil moisture data and to inform those who aim to develop new methods or improve existing ones. Core Ideas Soil moisture data can be used to identify the occurrence of preferential flow (PF) and its initiating conditions. Various data‐analysis methods to identify PF differ in susceptibility to error, bias, and subjectivity. These methods can utilize vast amounts of data from soil moisture monitoring networks to develop understanding of when, where, and under what conditions PF occurs. Newly developed methods may lead to better accuracy and reliability, and reduce the need for subjective judgments. Plain Language Summary Preferential flow through soil occurs when a large amount of water is suddenly available, as during an intense storm. This type of flow moves rapidly through the soil in distinct narrow pathways rather than moving evenly throughout the body of soil, with major consequences for groundwater resources, ecosystems, spreading of contaminants, and other vital concerns. Methods of detecting preferential flow have been developed that utilize measurements of soil water content made by sensors installed at various depths. This measurement technology has been widely implemented, many locations now having datasets years in length, and various methods have been developed for using these to identify preferential flow. The various methods are based on different features in the soil moisture records and vary in their advantages and shortcomings. In this review, we explain and evaluate these methods, highlighting important information for their implementation to identify preferential flow from soil moisture data and for efforts to develop new methods or improve existing ones.
Nimmo, John R↗
Electromagnetic-Tracked Biopsy under Ultrasound Guidance: Preliminary Results
Purpose: This study was designed to evaluate the accuracy and safety of electromagnetic needle tracking for sonographically guided percutaneous liver biopsies. Methods: We performed 23 consecutive ultrasound-guided liver biopsies for liver nodules with an electromagnetic tracking of the needle. A sensor placed at the tip of a sterile stylet (18G) inserted in a coaxial guiding trocar (16G) used for biopsy was localized in real time relative to the ultrasound imaging plane, thanks to an electromagnetic transmitter and two sensors on the ultrasound probe. This allows for electronic display of the needle tip location and the future needle path overlaid on the real-time ultrasound image. Distance between needle tip position and its electronic display, number of needle punctures, number of needle pull backs for redirection, technical success (needle positioned in the target), diagnostic success (correct histopathology result), procedure time, and complication were evaluated according to lesion sizes, depth and location, operator experience, and 'in-plane' or 'out-of-plane' needle approach. Results: Electronic display was always within 2 mm from the real position of the needle tip. The technical success rate was 100%. A single needle puncture without repuncture was used in all patients. Pull backs were necessary in six patients (26%) to obtain correct needle placement. The overall diagnostic success rate was 91%. The overall true-positive, true-negative, false-negative, and failure rates of the biopsy were 100% (19/19) 100% (2/2), 0% (0/23), and 9% (2/23). The median total procedure time from the skin puncture to the needle in the target was 30 sec (from 5-60 s). Lesion depth and localizations, operator experience, in-plane or out-of-plane approach did not affect significantly the technical, diagnostic success, or procedure time. Even when the tumor size decreased, the procedure time did not increase. Conclusions: Electromagnetic-tracked biopsy is accurate to determine needle tip position and allows fast and accurate needle placement in targeted liver nodules.
62 RADIOLOGY AND NUCLEAR MEDICINE↗