Coverage Metrics Research Articles

Functional verification of a sigma-delta ADC real number model

ABSTRACT Mixed-signal applications form one of the hottest topics in the semiconductor industry. Considerable effort is required for the creation of designs that consist of both analog and digital blocks with high accuracy and performance. Therefore, mixed-signal verification can be considered as a significant matter. Former conventional verification approaches present very slow verification time, which ultimately increases time-to-market. A circuit of interest in modern systems is the sigma-delta analog-to-digital converter (ADC), which is used for encoding analog signals into digital codes, and many other applications. In this work, an effective functional verification architecture using Universal Verification Methodology (UVM) for a SystemVerilog-based sigma-delta ADC real-number model is proposed. The UVM effectiveness of the presented approach encourages the creation of a verification architecture with increased reusability and robustness, with respect to previous literature. The presented verification environment utilises constrained-random stimuli that exploit a novel sine-wave generator, analog assertions and coverage metrics for improved functional verification. Additionally, a metric for verification quality estimation is introduced for comparisons with other reference verification approaches. In all cases, the presented architecture verified the proper operation of the sigma-delta ADC with coverage of more than 98%, in accordance with the specification parameters that were used for the model.

Parallel simulation of cyber-physical systems

Model-based design (MBD) in systems engineering is a well-accepted technique to abstract, analyze, verify, and validate complex systems. In MBD, we design a mathematical model of the system to virtually execute and test systems via model simulations to understand the system dynamics better. Computing model simulations has their challenges; one is to ensure that the simulation trajectory preserves the model semantics. Besides, computing many simulation trajectories over a long time-horizon must be time efficient for rapid respond to system engineers. In this work, we address these challenges in simulating models of cyber-physical systems (CPS), particularly systems possessing mixed discrete–continuous dynamics. We focus on the subclass of CPS’s hybrid automata models, where Jump predicates are restricted to polygonal constraints and present a numerical simulation engine that can efficiently compute many random simulations in parallel by exploiting the parallel computing capability in modern multicore processors. Our simulation engine implements a lock-free parallel breadth-first-search (BFS)-like algorithm and is implemented in the model-checking tool XSpeed. In addition, an application of our simulation engine in property verification of CPS models has been illustrated on two benchmarks. Some model coverage metrics have been defined that users of the tool can specify to set the desired thoroughness of testing with simulations. We demonstrate the performance gains of our simulation engine over SpaceEx and CORA, the modern model checkers and simulators for affine hybrid systems.

Plan quality effects of maximum monitor unit constraints in pencil beam scanning proton therapy for central nervous system and skull base tumors

Purpose/objective(s)With reports of CNS toxicity in patients treated with proton therapy at doses lower than would be expected based on photon data, it has been proposed that heavy monitor unit (MU) weighting of pencil beam scanning (PBS) proton therapy spots may potentially increase the risk of toxicity. We evaluated the impact of maximum MU weighting per spot (maxMU/spot) restrictions on PBS plan quality, prior to implementing clinic-wide maxMU/spot restrictions. Materials/methodsPBS plans of 11 patients, of which 3 plans included boosts, for a total of 14 PBS sample cases were included. Per sample case, a single dosimetrist created 4 test plans, gradually reducing the maxMU/spot in the plan. Test Plan 1, unrestricted in maxMU/spot, was the reference for all restricted plan comparisons (comparison sets 2 vs. 1; 3 vs. 1; and 4 vs. 1). The impact of MU/spot restrictions on plan quality metrics were analyzed with Wilcoxon signed rank test analyses. Treatment delivery time was modeled for a representative case. ResultsA total of 14 PBS sample cases, 7 (50%) single-field optimized, 7 (50%) multi-field optimized, 9 (64%) delivering > 3500 cGy, 9 (64%) with 3 beams, and 7 (50%) without a range shifter were included. There were no differences in plan quality metrics of target coverage (V95% and V100% prescription), conformality and gradient indices, hot spot volume (V105% prescription), and dose to normal brain (V10%/30%/50%/70%/90%/100% prescription) with reductions of allowable maxMU/spot across all comparison sets (p > 0.05). Max MU/spot restrictions did not increase treatment delivery time when analyzed for a representative case. ConclusionMaxMU/spot restrictions within the thresholds evaluated in this study did not degrade overall plan quality metrics. Future studies should evaluate spot weighting with linear energy transfer/relative biologic effectiveness-informed planning to determine if spot weighting manipulation impacts clinical outcomes and mitigates toxicity.

Clinical Acceptability of Automated Radiation Treatment Planning for Head and Neck Cancer Using the Radiation Planning Assistant

PurposeRadiation treatment planning for head and neck cancer is a complex process with much variability; automated treatment planning is a promising option to improve plan quality and efficiency. This study compared radiation plans generated from a fully automated radiation treatment planning system to plans generated manually that had been clinically approved and delivered. Methods and MaterialsThe study cohort consisted of 50 patients treated by a specialized head and neck cancer team at a tertiary care center. An automated radiation treatment planning system, the Radiation Planning Assistant, was used to create autoplans for all patients using their original, approved contours. Common dose-volume histogram (DVH) criteria were used to compare the quality of autoplans to the clinical plans. Fourteen radiation oncologists, each from a different institution, then reviewed and compared the autoplans and clinical plans in a blinded fashion. ResultsAutoplans and clinical plans were very similar with regard to DVH metrics for coverage and critical structure constraints. Physician reviewers found both the clinical plans and autoplans acceptable for use; overall, 78% of the clinical plans and 88% of the autoplans were found to be usable as is (without any edits). When asked to choose which plan would be preferred for approval, 27% of physician reviewers selected the clinical plan, 47% selected the autoplan, 25% said both were equivalent, and 0% said neither. Hence, overall, 72% of physician reviewers believed the autoplan or either the clinical or autoplan was preferable. ConclusionsAutomated radiation treatment planning creates consistent, clinically acceptable treatment plans that meet DVH criteria and are found to be appropriate on physician review.

Offline evaluation of the recommender system initial user interview using estimation of the coverage of content characteristics

Recommender systems are used everywhere in everyday life and have been of interest to researchers for many years. In recent years, many new papers have been published in this area, but the problem of cold start remains one of the most significant problems of recommender systems which use historical user data. The simplest solution is to do an initial user interview, but existing solutions for selecting items for it do not use a recommender model. However, they work with it, and they can improve the efficiency by using it for generating interview. This paper proposes adaptive approaches for selecting items for an initial interview based on the model, predictability of users and usefulness of items for a model. We also show how to build an initial interview for a new user by historical information about interactions of other users with items and the corresponding ratings for those interactions only. This is important because the information about content attributes is often missing or unreliable, and the developer is often not aware of specific characteristics that initial interview need to cover. This article and the described research are part of a large project related to the study of the problem of cold start and other problems of recommender systems with a large number of objects and a small number of interactions. As part of this project, we are developing application with which we will continue our research and estimate initial interview with real users. In this paper, we describe the content coverage metrics and demonstrate that our method requires less number of initial user interview screens to cover it than most others. The proposed method requires only three screens to cover 92.9% of the coverage metrics, and only four to cover all, on our dataset which is very sparsed and has 99.99% of missing values.

A Facility Location and Allocation Model for Cooperative Fire Services

Designing efficient urban fire service systems is of crucial importance as prompt responses to emergencies and accidents can drastically reduce property loss and mortality. To achieve these goals, holistic location-allocation models for cooperative fire services must consider multiple factors, such as fire station size, vehicle quantity, vehicle type, response time, service reliability, and traffic condition. This paper proposes a mixed-integer linear program model that takes these factors into account by using two coverage metrics: one related to vehicle coverage, indicating service reliability, and the other related to time coverage, describing service accessibility. Integrating these two metrics, a full or partial time and vehicle coverage (FPTVC) standard is obtained that characterizes the efficiency of response of cooperative fire services. A case study and multiple sensitivity analyses based on historical data from Hefei (China) are performed to demonstrate the relationships between vehicle coverage proportion, time coverage rate, fire station number, fire engine quantity, FPTVC, and total budget. The solutions obtained are further analyzed using GIS-based maps, which validate that the proposed model can help develop enhanced fire service systems designs.

An Improved Multi-Objective Particle Swarm Optimization With TOPSIS and Fuzzy Logic for Optimizing Trapezoidal Labyrinth Weir

Labyrinth Weir (LW) is a popular control structure that passes a significantly higher flow rate compared to the linear weirs. In order to approach the optimal design of a trapezoidal LW, a multi-objective problem is defined to concurrently minimize the LW consumed concrete volume and maximize its discharge capacity. Simultaneously, a Radial Basis function Neural Networks (RBFNN) is designed and used for estimating LW discharge coefficient (Cd) according to the existing experimental results. An improved multi-objective particle swarm optimization (MOPSO) algorithm named TOPSIS Fuzzy MOPSO (TFMOPSO) is proposed to solve the LW optimization problem. This algorithm utilizes the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank the solutions, while a fuzzy inference system is developed to select the algorithm strategy for finding two leaders among the non-dominated solutions. The performance of the proposed TFMOPSO has been tested on the optimization problem of the LW of the Ute dam. The results of TFMOPSO, along with three other state-of-the-art multi-objective algorithms, are explored in terms of hypervolume, coverage, and spacing metrics. It is demonstrated that the TFMOPSO outperforms other algorithms and studies for solving the LW multi-objective optimization problem for the case of Ute dam. Also, RBFNN is found to be one of the most appropriate approaches among studied algorithms in estimating the discharge coefficient of LW, while Pareto optimal solutions from TFMOPSO exhibit a significant improvement compared to the original design of Ute dam LW.

Development and clinical validation of a robust knowledge-based planning model for stereotactic body radiotherapy treatment of centrally located lung tumors.

PurposeTo develop a robust and adaptable knowledge‐based planning (KBP) model with commercially available RapidPlanTM for early stage, centrally located non‐small‐cell lung tumors (NSCLC) treated with stereotactic body radiotherapy (SBRT) and improve a patient's“simulation to treatment” time.MethodsThe KBP model was trained using 86 clinically treated high‐quality non‐coplanar volumetric modulated arc therapy (n‐VMAT) lung SBRT plans with delivered prescriptions of 50 or 55 Gy in 5 fractions. Another 20 independent clinical n‐VMAT plans were used for validation of the model. KBP and n‐VMAT plans were compared via Radiation Therapy Oncology Group (RTOG)–0813 protocol compliance criteria for conformity (CI), gradient index (GI), maximal dose 2 cm away from the target in any direction (D2cm), dose to organs‐at‐risk (OAR), treatment delivery efficiency, and accuracy. KBP plans were re‐optimized with larger calculation grid size (CGS) of 2.5 mm to assess feasibility of rapid adaptive re‐planning.ResultsKnowledge‐based plans were similar or better than n‐VMAT plans based on a range of target coverage and OAR metrics. Planning target volume (PTV) for validation cases was 30.5 ± 19.1 cc (range 7.0–71.7 cc). KBPs provided an average CI of 1.04 ± 0.04 (0.97–1.11) vs. n‐VMAT plan'saverage CI of 1.01 ± 0.04 (0.97–1.17) (P < 0.05) with slightly improved GI with KBPs (P < 0.05). D2cm was similar between the KBPs and n‐VMAT plans. KBPs provided lower lung V10Gy (P = 0.003), V20Gy (P = 0.007), and mean lung dose (P < 0.001). KBPs had overall better sparing of OAR at the minimal increased of average total monitor units and beam‐on time by 460 (P < 0.05) and 19.2 s, respectively. Quality assurance phantom measurement showed similar treatment delivery accuracy. Utilizing a CGS of 2.5 mm in the final optimization improved planning time (mean, 5 min) with minimal or no cost to the plan quality.ConclusionThe RTOG‐compliant adaptable RapidPlan model for early stage SBRT treatment of centrally located lung tumors was developed. All plans met RTOG dosimetric requirements in less than 30 min of planning time, potentially offering shorter “simulation to treatment” times. OAR sparing via KBPs may permit tumorcidal dose escalation with minimal penalties. Same day adaptive re‐planning is plausible with a 2.5‐mm CGS optimizer setting.

A rapid, cost-effective tailed amplicon method for sequencing SARS-CoV-2

BackgroundThe global COVID-19 pandemic has led to an urgent need for scalable methods for clinical diagnostics and viral tracking. Next generation sequencing technologies have enabled large-scale genomic surveillance of SARS-CoV-2 as thousands of isolates are being sequenced around the world and deposited in public data repositories. A number of methods using both short- and long-read technologies are currently being applied for SARS-CoV-2 sequencing, including amplicon approaches, metagenomic methods, and sequence capture or enrichment methods. Given the small genome size, the ability to sequence SARS-CoV-2 at scale is limited by the cost and labor associated with making sequencing libraries.ResultsHere we describe a low-cost, streamlined, all amplicon-based method for sequencing SARS-CoV-2, which bypasses costly and time-consuming library preparation steps. We benchmark this tailed amplicon method against both the ARTIC amplicon protocol and sequence capture approaches and show that an optimized tailed amplicon approach achieves comparable amplicon balance, coverage metrics, and variant calls to the ARTIC v3 approach.ConclusionsThe tailed amplicon method we describe represents a cost-effective and highly scalable method for SARS-CoV-2 sequencing.

Comparison of Cardiopulmonary Sparing and Dose Conformality in Distal Esophageal Cancer: Intensity Modulated Proton Therapy (IMPT), MR-guided Radiotherapy (MRgRT), and Volumetric Modulated Arc Therapy (VMAT)

To investigate the tradeoff of esophageal treatment between real-time breath hold (BH) MR-guided radiotherapy (MRgRT), free breathing (FB) CT-based intensity modulated proton therapy (IMPT), and FB VMAT. We hypothesize that improved cardiopulmonary sparing would be achieved with BH MRgRT or FB IMPT compared to FB VMAT. We retrospectively evaluated differences in heart/lung dose and treatment plan conformality among 28 patients with distal esophageal cancer who were each treated with either IMPT (n = 10), MRgRT on an MR-guided Linac (n = 11), or VMAT (n = 7). All were prescribed 50.4 Gy/28 fractions. IMPT and VMAT patients received a 4DCT simulation scan to quantify the internal target volume margin (ITV) for respiratory motion. MRgRT patients were simulated and treated in an inspiration BH with a 3 mm CTV to PTV margin. In lieu of a PTV margin for IMPT cases, robust optimization for range and setup uncertainty was performed on iCTV (i.e., CTV with respiratory ITV). RTOG plan quality metrics were used to evaluate target coverage (TC) (PTV V100%/PTV vol), homogeneity index (HI) (PTV D2%/ D98%), high dose conformity (PITV), low dose conformity (D2cm), and gradient (R50%). For all coverage metrics, the CTV was used. Mean cardiac sparing among all patients between respective IMPT, MRgRT, and VMAT was 65.3cc, 73.0cc, and 141.5cc (V30Gy); 45.4cc, 38.5cc, and 84.7cc (V40Gy), 19.7cc, 12.5cc, 39.0cc, (V50Gy), 6.4cc, 15.0cc, 19.2cc (mean dose). Lung and liver sparing between respective IMPT, MRgRT and VMAT plans was 86.8cc, 299.7cc, and 909.0cc (lung, V20), 4.2Gy, 6.8Gy, and 15.6Gy (lung, mean dose), 3.8Gy, 19.5Gy, 16.2Gy (liver, mean dose). Homogeneity and conformity indices between IMPT, MRgRT, and VMAT were 1.6, 1.3, 1.5 (PITV), 1.1, 1.2, 1.1 (HI), 5.2, 5.2, 6.3 (R50%), and 45.3%, 56.6%, 54.1%(D2cm), respectively. CTV coverages for IMPT and MRgRT plans were 50.7, 50.5, 51.1 (D90%) and 0.99, 0.99, 0.98 (TC), respectively. Statistically significant differences in heart and lung dose are demonstrated in the Table below; no statistical difference was observed for coverage or conformality. FB IMPT and BH MRgRT both achieve significant lung and/or heart sparing compared to FB VMAT while maintaining excellent plan conformality. The varying extent of cardiopulmonary sparing achieved across these advanced radiation therapy modalities warrants prospective evaluation.Abstract 3498; TableHeart (V30)Heart (V40)Heart (V50)Heart (Mean)Lung (Mean)Lung (V20)Liver (Mean)MRgRT vs IMPTp = 0.695p = 0.602p = 0.792p = 0.0001p = 0.079p = 0.001p = 0.0001MRgRT vs VMATp = 0.048p = 0.058p = 0.053p = 0.101p = 0.0001p = 0.001p = 0.258IMPT vs VMATp = 0.031p = 0.134p = 0.218p = 0.0001p = 0.0001p = 0.0002p = 0.001 Open table in a new tab

Evaluating Maximum MU per Spot Thresholds on CNS Pencil Beam Proton Therapy Plan Quality

There are concerning reports of CNS toxicity in patients treated with proton therapy at doses lower than would be expected based on photon data estimates. Given the desire to mitigate risk of serious unexpected events, we propose limiting the maximum monitor unit weighting per spot (maxMU/spot) to the entire plan with the hypothesis that heavily weighted spots may pose an unforeseen risk of toxicity despite meeting objective dose constraints. This dosimetric study assesses the impact of maxMU/spot restrictions on pencil beam proton therapy (PBPT) plan quality. We hypothesize that plan quality may be preserved while still restricting the maxMU/spot, but there may be a threshold below which restrictions negatively impact plan quality. PBPT plans of 11 patients (9 consecutive, 2 additional with notably challenging plan objectives) were included, of which 3 plans included boosts, for a total of 14 nominal PBPT cases. Per nominal plan, a single dosimetrist created 4 test plans as outlined in Table 1, iteratively reducing the maxMU/spot in the plan. Test Plan 1, unrestricted in maxMU/spot, was the reference for all analyses (comparison sets 2 vs. 1; 3 vs. 1; and 4 vs. 1). The differences in each plan quality metric in each comparison set were analyzed using the Wilcoxon signed rank test, with p<0.05 significance testing. Beam-specific metrics in each comparison set were analyzed, defined by the root mean square deviation in beam metrics from the unrestricted plan. Linear regression with log transformation was performed to analyze the impact of restriction conditions on changes in the beam-specific max dose, heterogeneity, and % of target receiving the Rx/#beams. A total of 14 PBPT plans, 7 (50%) SFO, 7 (50%) MFO, 9 (64%) delivering >3500 cGy, 9 (64%) with 3 beams, 5 (36%) with 4-5 beams, and 7 (50%) without a range shifter were included. With increasing MU restriction, there were no differences in plan quality metrics of target coverage (V95% and V100%Rx), conformality and gradient indices, hot spot volume (V105%Rx), and dose to unaffected brain (V10%/30%/50%/70%/90%/100%Rx) across all comparison sets (p>0.05). However, there was a mean increase of 854 (2 vs. 1, p = 0.02), 869 (3 vs. 1, p = 0.025), and 892 (4 vs. 1, p = 0.007) total spots in the more restricted MU plans. MaxMU restriction did not significantly impact the beam-specific max dose (p = 0.259), heterogeneity (p = 0.305), and % of target receiving the Rx/#beams (p = 0.409). MaxMU/spot restriction did not negatively impact overall plan quality and beam-specific metrics within the thresholds evaluated in this study. Future studies should evaluate spot weighting with LET/RBE to determine if spot weighting manipulation may truly impact clinical outcomes and mitigate toxicity.Tabled 1Abstract 2601; Table; Iterative reduction maximum allowable MU per spot*PlanNo Range shifter (RS)2cm RS3cm RS5cm RS1No limitNo limitNo limitNo limit230405070320304060410203050∗1 MU = 1 cGY/mm2 at a 2 cm for monoenergetic proton beam Open table in a new tab

Daily Dosimetric Robustness of Prone-Position Whole Breast Radiotherapy (WBRT) Planned With Electronic Tissue Compensation (ECOMP) and 6MV Flattening Filter Free (6X-FFF) Beam Energy: Dose Variation and Predictive Factors

This study investigates daily dose to prone-positioned patients undergoing tangential WBRT on an O-ring LINAC with 6X-FFF energy. For prone breast patients, most practices rely on skin marks or daily planar image matching. This system provides low dose (<1mGy CTDIvol) daily CBCT, which was used to verify daily robustness of target coverage and hotspot dose metrics for prone-position WBRT. Eight patients treated with 16-fraction prone breast WBRT planned with ECOMP were retroactively studied. During treatment, patients were positioned under CBCT guidance by matching to the chest wall. Planning CTs were deformed to daily CBCT to generate daily synthetic CTs, on which daily dose distributions were calculated. A total of 8 patients x 16 fractions = 128 synthetic CTs were generated. Consensus ASTRO definition was used to contour Breast PTV Eval for each daily deformed CT. Target coverage (V90%) and hotspot (V105%) to Breast PTV Eval contour was monitored each day. Various predictors (patient weight, breast separation, breast deformation / dice similarity coefficient DSC, etc.) were fit into a mixed effect linear regression model predicting V90% and V105% deviation from planned values (ΔV90%, ΔV105%). Statistical significance is indicated with asterisks (* for p<0.05; ** for p<0.001). For all synthetic CTs studied, V90% (as % of Breast PTV Eval volume) was moderately smaller than planned (median ΔV90% = -0.1%*), while V105% was much larger (median ΔV105% = +10.1%** or +92.4cc**). Among all fractions studied, only 40% met PTV Breast Eval V105%<15% criteria and 59% met V90%>95%. V90% and V105% did not correlate with time. Breast PTV Eval volume decreased with time (-2.21cc per fx*), possibly indicating general weight loss or seroma resolution. Patient’s weight loss correlated with significantly increased V105% (+4.6% / -1% weight., R = 0.4**) and moderately decreased V90% (-0.071% / -1% wt., R = 0.2**). However, comprehensive models indicate three factors influence ΔV90% and ΔV105% the most (with R = 0.86**, 0.95** respectively): (1) Breast separation decrease (-0.09%* and +10%** per -1cm), (2) PTV Eval volume decrease (-0.4%** and +9%** per -100cc), and for ΔV105% only, (3) more breast deformation (+10%** per -1% ΔDSC). For patients undergoing tangential prone breast WBRT with ECOMP-planned parallel opposed beams, daily CBCT revealed key dosimetric parameters vary significantly, especially for V105% associated with negative cosmesis. Patient weight loss substantially increased hotspots and slightly decreased coverage over treatment course. Breast separation and shape changes could be used to predict dosimetric variations. Preliminary findings suggest some patients could benefit from an adaptive plan based on daily CBCT.

LoSeRO: A Locality Sensitive Routing Protocol in Opportunistic Networks with Contact Profiles

Mobility trajectories of users contain personal information that when analyzed may reveal relevant data usable as message-sharing condition, e.g., interests in common or similar mobility patterns. In particular, leveraging on mobility patterns, in [12] we designed and presented a Geo-casting routing protocol called LoSeRO for opportunistic networks, which uses knowledge of the locations most frequently visited by a user to route messages. LoSeRO forwards messages—in a multi-casting way—to all users who have a mobility profile that intersects the packet's destination zone. LoSeRO presented a relative good performance value, however, to improve its performances, in this paper our contribution is to propose an upgraded version of our earlier proposed protocol, termed as LoSeRO v2 . In particular, it upgrades the traditional working fashion of LoSeRO by extending the knowledge of the most frequented locations to those users not only directly met, i.e., two-hops away. With this purpose, through simulations, we compare the performance of LoSeRO v2, with LoSeRO and other existing routing geo-casting protocols, and we illustrate how LoSeRO v2 achieves enhanced performances comparing precision, coverage and additional metrics.

A multi-objective optimization framework of constellation design for emergency observation

To meet the urgent needs of emergency observation missions, a well-designed constellation is key to ensuring system performance. This paper presents a multi-objective optimization framework which is well suited for constellation designers to identify key trade-offs and make decisions. Firstly, this work investigates the metrics of responsiveness and coverage simultaneously to approximate to the actual conditions of emergencies, and a multi-objective mathematical model is constructed to explore solutions to enhance the overall system performance. Then, according to the characteristics of emergency missions, a hierarchical chromosome encoding method is proposed in the framework to encode a solution that explores different sized constellations without constraining its specific geometry. Finally, we propose the use of radial axes plots to help constellation designers to gain pragmatic insights for informed decision making. The simulation of disaster management verifies that the proposed chromosome encoding method effectively promotes the convergence of the algorithm, and the multi-objective optimization framework helps to generate a rapid and adequate observation satellite constellation to provide maximum coverage performance while ensuring timeliness.

Feasibility of hippocampal avoidance whole brain radiation in patients with hippocampal involvement: Data from a prospective study

PurposeAmong patients with brain metastases, hippocampal avoidance whole brain radiation (HA-WBRT) preserves neurocognitive function relative to conventional WBRT but the feasibility of hippocampal sparing in patients with metastases in/near the hippocampus is unknown. We identified the incidence of hippocampal/perihippocampal metastases and evaluated the feasibility of HA-WBRT in such patients. Materials/MethodsDosimetric data from 34 patients randomized to HA-WBRT (30 Gy/10 fractions) in a phase III trial (NCT03075072) comparing HA-WBRT to stereotactic radiation in patients with 5 to 20 brain metastases were analyzed. Patients with metastases in/near the hippocampi received HA-WBRT with prioritization of tumor coverage over hippocampal avoidance. Target coverage and hippocampal sparing metrics were compared between patients with targets in/near the hippocampus versus not. ResultsIn total, 9 of 34 (26%) patients had targets in the hippocampus and an additional 5 of 34 (15%) patients had targets in the hippocampal avoidance zone (HAZ, hippocampus plus 5 mm expansion) but outside the hippocampus. Patients with targets within the hippocampus and those with targets in the HAZ but outside the hippocampus were spared 34% and 73% of the ipsilateral mean biologically equivalent prescription dose, respectively. Of the latter cohort, 88% and 25% met conventional hippocampal sparing metrics of Dmin ≤ 9 Gy and Dmax ≤ 16 Gy, respectively. Among 11 patients with unilateral hippocampal/perihippocampal involvement, the uninvolved/contralateral hippocampus was limited to Dmin ≤ 9 Gy and Dmax ≤ 17 Gy in all cases. ConclusionsIn this study, a substantial percentage of patients with 5 to 20 brain metastases harbored metastases in/near the hippocampus. In such cases, minimizing hippocampal dose while providing tumor coverage was feasible and may translate to neurocognitive protection.

In Vegetation Growth Studies, What You Measure Matters

Different satellite-based metrics for global vegetation coverage tell complementary, but not identical, stories.

Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach.

Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.

Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques

Abstract. Vegetation has a tremendous influence on snow processes and snowpack dynamics, yet remote sensing techniques to resolve the spatial variability of sub-canopy snow depth are not always available and are difficult from space-based platforms. Unmanned aerial vehicles (UAVs) have had recent widespread application to capture high-resolution information on snow processes and are herein applied to the sub-canopy snow depth challenge. Previous demonstrations of snow depth mapping with UAV structure from motion (SfM) and airborne lidar have focussed on non-vegetated surfaces or reported large errors in the presence of vegetation. In contrast, UAV-lidar systems have high-density point clouds and measure returns from a wide range of scan angles, increasing the likelihood of successfully sensing the sub-canopy snow depth. The effectiveness of UAV lidar and UAV SfM in mapping snow depth in both open and forested terrain was tested in a 2019 field campaign at the Canadian Rockies Hydrological Observatory, Alberta, and at Canadian prairie sites near Saskatoon, Saskatchewan, Canada. Only UAV lidar could successfully measure the sub-canopy snow surface with reliable sub-canopy point coverage and consistent error metrics (root mean square error (RMSE) &lt;0.17 m and bias −0.03 to −0.13 m). Relative to UAV lidar, UAV SfM did not consistently sense the sub-canopy snow surface, the interpolation needed to account for point cloud gaps introduced interpolation artefacts, and error metrics demonstrated relatively large variability (RMSE&lt;0.33 m and bias 0.08 to −0.14 m). With the demonstration of sub-canopy snow depth mapping capabilities, a number of early applications are presented to showcase the ability of UAV lidar to effectively quantify the many multiscale snow processes defining snowpack dynamics in mountain and prairie environments.

Finding Community of Brain Networks Based on Neighbor Index and DPSO with Dynamic Crossover

Background: : The brain networks can provide us an effective way to analyze brain function and brain disease detection. In brain networks, there exist some import neural unit modules, which contain meaningful biological insights. Objective:: Therefore, we need to find the optimal neural unit modules effectively and efficiently. Method:: In this study, we propose a novel algorithm to find community modules of brain networks by combining Neighbor Index and Discrete Particle Swarm Optimization (DPSO) with dynamic crossover, abbreviated as NIDPSO. The differences between this study and the existing ones lie in that NIDPSO is proposed first to find community modules of brain networks, and dose not need to predefine and preestimate the number of communities in advance. Results: : We generate a neighbor index table to alleviate and eliminate ineffective searches and design a novel coding by which we can determine the community without computing the distances amongst vertices in brain networks. Furthermore, dynamic crossover and mutation operators are designed to modify NIDPSO so as to alleviate the drawback of premature convergence in DPSO. Conclusion: The numerical results performing on several resting-state functional MRI brain networks demonstrate that NIDPSO outperforms or is comparable with other competing methods in terms of modularity, coverage and conductance metrics.

Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach

Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p&lt;0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.