Coverage Metrics Research Articles

Assessing progress towards viral hepatitis B and C elimination in prisons in the European region

Abstract Background In 2016 the WHO launched the global health sector strategy for the elimination of viral hepatitis, alongside a dedicated monitoring&evaluation (M&E) framework to assess progress at national, regional and global levels. Despite being a priority setting for viral hepatitis elimination in Europe, prisons are rarely integrated into national monitoring efforts. Besides, prisons are particular contexts with a high viral hepatitis prevalence, where it is possible to utilize adequate micro-elimination strategies. Objectives We aimed to assess available metrics of viral hepatitis burden and coverage of prevention and control interventions in prison settings in Europe using routine sources of data. Methods We adapted the WHO M&E framework for viral hepatitis elimination to the prison context. The resulting M&E framework has 49 indicators of which 10 are defined as core. Data were sourced from available European prison surveillance systems at European level, namely: European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) and WHO Health in Prisons European Database (HIPED). Results In total 38 countries in the European region provided data to either data sources for 2020. For WHO-HIPED, among core indicators, 3 out of 10 could be calculated for HCV, and covered prevalence, HCV treatment availability and treatment outcome. Concerning HBV, 3 core indicators out of 10 could be calculated and covered prevalence, screening availability and treatment coverage in prison. With EMCDDA surveillance system we could calculate 4 HCV core indicators and 6 HBV core indicators. Lessons Availability of health data from prison settings is scarce, hampering the current capacity to monitor the progress in the viral hepatitis elimination within prisons contexts strategies and its relative contribution to global health goals achievement. A coordinated and standardised M&E framework tailored to this specific setting is needed to foster data collection and systematic monitoring. Key messages • Micro-elimination strategies in prison context contribute to the global health goals achievement. • A coordinated and standardized M&V framework, adapted to the prison context, is needed to foster data collection and monitoring of the viral hepatitis elimination strategy.

Comparison of translation algorithms in determining maximum allowable CTV shifts for Real-Time Gated Proton Therapy (RGPT) robustness evaluation in prostate cancers.

Real-Time Gated Proton Therapy (RGPT) is an active motion management technique that utilizes treatment gating and tumor tracking via fiducial markers. When performing RGPT treatment for prostate cancer, it is essential to account for the CTV displacement relative to the body in the clinical workflow. The workflow at the National Cancer Centre Singapore (NCCS) includes bone matching via CT-CBCT images, followed by fiducial matching via pulsed fluoroscopy (soft tissue matching), and finally, a robustness evaluation procedure to determine if the difference is within an allowable tolerance. In this study, we compare two CTV translation methods for robustness evaluation: (1) an in-house translation algorithm and (2) the RayStation "simulate organ motion" Deformable image registration (DIR) algorithm. Nine RGPT prostate patient plans with CTV volumes ranging from 17.1 to 96.72 cm2 were included in this study. An in-house translation algorithm and "simulate organ motion" DIR RayStation algorithm were used to generate CTV shifts along R-L, I-S, and P-A axes between 10mm at 2mm steps. At each step, dose metrics, which include CTV Dmax, CTV D95%, and CTV D98%, were extracted and used as comparative metrics for CTV target coverage and hot spot evaluation. Across all axes, there were no statistically significant differences between the two algorithms for all three dose metrics: CTV Dmax (P=0.92, P=0.91, and P=0.47), CTV D95% (P=0.97, P=0.22, and P=0.33), and CTV D98% (P=0.85, P=0.33, and P=0.36). Further, the in-house translation algorithm evaluation time was less than 10s, two orders of magnitude faster than the DIR algorithm. Our results demonstrate that the simpler in-house algorithm performs equivalently to the realistic DIR algorithm when simulating CTV motion in prostate cancers. Furthermore, the in-house algorithm completes the robustness evaluation two orders of magnitude faster than the DIR algorithm. This significant reduction in evaluation time is crucial especially when preparatory time efficiency is of paramount importance in a busy clinic.

Joint Metrics for EMF Exposure and Coverage in Real-World Homogeneous and Inhomogeneous Cellular Networks

Joint Metrics for EMF Exposure and Coverage in Real-World Homogeneous and Inhomogeneous Cellular Networks

Commonly used genomic arrays may lose information due to imperfect coverage of discovered variants for autism spectrum disorder

BackgroundCommon genetic variation has been shown to account for a large proportion of ASD heritability. Polygenic scores generated for autism spectrum disorder (ASD-PGS) using the most recent discovery data, however, explain less variance than expected, despite reporting significant associations with ASD and other ASD-related traits. Here, we investigate the extent to which information loss on the target study genome-wide microarray weakens the predictive power of the ASD-PGS.MethodsWe studied genotype data from three cohorts of individuals with high familial liability for ASD: The Early Autism Risk Longitudinal Investigation (EARLI), Markers of Autism Risk in Babies-Learning Early Signs (MARBLES), and the Infant Brain Imaging Study (IBIS), and one population-based sample, Study to Explore Early Development Phase I (SEED I). Individuals were genotyped on different microarrays ranging from 1 to 5 million sites. Coverage of the top 88 genome-wide suggestive variants implicated in the discovery was evaluated in all four studies before quality control (QC), after QC, and after imputation. We then created a novel method to assess coverage on the resulting ASD-PGS by correlating a PGS informed by a comprehensive list of variants to a PGS informed with only the available variants.ResultsPrior to imputations, None of the four cohorts directly or indirectly covered all 88 variants among the measured genotype data. After imputation, the two cohorts genotyped on 5-million arrays reached full coverage. Analysis of our novel metric showed generally high genome-wide coverage across all four studies, but a greater number of SNPs informing the ASD-PGS did not result in improved coverage according to our metric.Limitations.The studies we analyzed contained modest sample sizes. Our analyses included microarrays with more than 1-million sites, so smaller arrays such as Global Diversity and the PsychArray were not included. Our PGS metric for ASD is only generalizable to samples of European ancestries, though the coverage metric can be computed for traits that have sufficiently large-sized discovery findings in other ancestries.ConclusionsWe show that commonly used genotyping microarrays have incomplete coverage for common ASD variants, and imputation cannot always recover lost information. Our novel metric provides an intuitive approach to reporting information loss in PGS and an alternative to reporting the total number of SNPs included in the PGS. While applied only to ASD here, this metric can easily be used with other traits.

Three-dimensional assessment of subchondral arc and hip joint coverage angles in the asymptomatic young adult hip.

The primary objective of this study was to develop a custom algorithm to assess three-dimensional (3D) acetabular coverage of the femoral head based on surface models generated from computed tomography (CT) imaging. The secondary objective was to apply this algorithm to asymptomatic young adult hip joints to assess the regional 3D acetabular coverage variability and understand how these novel 3D metrics relate to traditional two-dimensional (2D) radiographic measurements of coverage. The algorithm developed automatically identifies the lateral- and medial-most edges of the acetabular lunate at one-degree intervals around the acetabular rim based on local radius of curvature. The acetabular edges and the center of a best-fit sphere to the femoral head are then used to compute the mean 3D subchondral arc angles and hip joint coverage angles in five acetabular octants. This algorithm was applied to hip models generated from pelvis/hip CT imaging or abdomen/pelvis CT angiograms of 50 patients between 17 and 25 years of age who had no history of congenital or developmental hip pathology, neuromuscular conditions, or bilateral pelvic and/or femoral fractures. Corresponding 2D acetabular coverage measures of lateral center edge angle (LCEA) and acetabular arc angle (AAA) were assessed on the patients' clinical or digitally reconstructed radiographs. The 3D subchondral arc angle in the superior region (58.0 [54.6-64.8] degrees) was significantly higher (p < 0.001) than all other acetabular subregions. The 3D hip joint coverage angle in the superior region (26.2 [20.7-28.5] degrees) was also significantly higher (p < 0.001) than all other acetabular subregions. 3D superior hip joint coverage angle demonstrated the strongest correlation with 2D LCEA (r = 0.649, p < 0.001), while 3D superior-anterior subchondral arc angle demonstrated the strongest correlation with 2D AAA (r = 0.718, p < 0.001). The 3D coverage metrics in the remaining acetabular regions did not strongly correlate with typical 2D radiographic measures. The discrepancy between standard 2D measures of radiographic acetabular coverage and actual 3D coverage identified on advanced imaging indicates potential discord between anatomic coverage and the standard clinical measures of coverage on 2D imaging. As 2D measurement of acetabular coverage is increasingly used to guide surgical decision-making to address acetabular deformities, this work would suggest that 3D measures of acetabular coverage may be important to help discriminate local coverage deficiencies, avoid inconsistencies resulting from differences in radiographic measurement techniques, and provide a better understanding of acetabular coverage in the hip joint, potentially altering surgical planning and guiding surgical technique.

A survey on node localization technologies in UWSNs: Potential solutions, recent advancements, and future directions

SummaryLocation‐based underwater communication applications such as strategic surveillance, disaster prevention, marine research, and mine detection have given the field of underwater wireless sensor networks (UWSN) a head start. Node localization is a prerequisite for accurate data collection, target monitoring, and network management in UWSNs. However, the unique characteristics of the underwater environment, such as signal attenuation, multipath propagation, and variable acoustic properties, pose a major challenge to effective node localization. Accurate sensor node location data is essential for successful underwater data collection, but difficult to achieve as the GPS system cannot be used in an underwater environment. In this paper, existing node localization techniques such as ALS, SLUM, MASL, SLMP, UDB, USP, etc., and recent advances such as the fusion of range‐based and range‐free techniques, the fusion of RSSI and AoA to improve localization accuracy by using directional information in addition to signal strength, and the use of optimization techniques such as PSO, COA, and WOA algorithms to improve the accuracy of the applied node localization algorithm, e.g., TP‐TSFLA, and challenges related to UWSN are discussed. Also, different localization algorithms that affect the accuracy of UWSN localization techniques have been evaluated and compared with NS2 in terms of localization error, localization coverage, energy consumption, and average communication cost metrics. In addition, this paper also provides an up‐to‐date investigation of localization techniques. Finally, the tools available for simulation are presented, followed by open research questions that need to be addressed in the localization of nodes.

Evaluation of a passive wearable arm ExoNET.

Wearable ExoNETs offer a novel, wearable solution to support and facilitate upper extremity gravity compensation in healthy, unimpaired individuals. In this study, we investigated the safety and feasibility of gravity compensating ExoNETs on 10 healthy, unimpaired individuals across a series of tasks, including activities of daily living and resistance exercises. The direct muscle activity and kinematic effects of gravity compensation were compared to a sham control and no device control. Mixed effects analysis revealed significant reductions in muscle activity at the biceps, triceps and medial deltoids with effect sizes of -3.6%, -4.5%, and -7.2% rmsMVC, respectively, during gravity support. There were no significant changes in movement kinematics as evidenced by minimal change in coverage metrics at the wrist. These findings reveal the potential for the ExoNET to serve as an alternative to existing bulky and encumbering devices in post-stroke rehabilitation settings and pave the way for future clinical trials.

Adaptive scheduling-based fine-grained greybox fuzzing for cloud-native applications

Coverage-guided fuzzing is one of the most popular approaches to detect bugs in programs. Existing work has shown that coverage metrics are a crucial factor in guiding fuzzing exploration of targets. A fine-grained coverage metric can help fuzzing to detect more bugs and trigger more execution states. Cloud-native applications that written by Golang play an important role in the modern computing paradigm. However, existing fuzzers for Golang still employ coarse-grained block coverage metrics, and there is no fuzzer specifically for cloud-native applications, which hinders the bug detection in cloud-native applications. Using fine-grained coverage metrics introduces more seeds and even leads to seed explosion, especially in large targets such as cloud-native applications. Therefore, we employ an accurate edge coverage metric in fuzzer for Golang, which achieves finer test granularity and more accurate coverage information than block coverage metrics. To mitigate the seed explosion problem caused by fine-grained coverage metrics and large target sizes, we propose smart seed selection and adaptive task scheduling algorithms based on a variant of the classical adversarial multi-armed bandit (AMAB) algorithm. Extensive evaluation of our prototype on 16 targets in real-world cloud-native infrastructures shows that our approach detects 233% more bugs than go-fuzz, achieving an average coverage improvement of 100.7%. Our approach effectively mitigates seed explosion by reducing the number of seeds generated by 41% and introduces only 14% performance overhead.

Fine-grained Coverage-based Fuzzing

Fuzzing is a popular software testing method that discovers bugs by massively feeding target applications with automatically generated inputs. Many state-of-the-art fuzzers use branch coverage as a feedback metric to guide the fuzzing process. The fuzzer retains inputs for further mutation only if branch coverage is increased. However, branch coverage only provides a shallow sampling of program behaviors and hence may discard interesting inputs to mutate. This work aims to take advantage of the large body of research in defining finer-grained code coverage metrics (such as control-flow, data-flow, or mutation coverage) and to evaluate how fuzzing performance is impacted when using these metrics to select interesting inputs for mutation. We propose to make branch coverage-based fuzzers support most fine-grained coverage metrics out of the box (i.e., without changing fuzzer internals). We achieve this by making the test objectives defined by these metrics (such as conditions to activate or mutants to kill) explicit as new branches in the target program. Fuzzing such a modified target is then equivalent to fuzzing the original target, but the fuzzer will also retain inputs covering the additional metric objectives for mutation. In addition, all the fuzzer mechanisms to penetrate hard-to-cover branches will help in covering the additional metric objectives. We use this approach to evaluate the impact of supporting two fine-grained coverage metrics (multiple condition coverage and weak mutation) over the performance of two state-of-the-art fuzzers (AFL++ and QSYM) with the standard LAVA-M and MAGMA benchmarks. This evaluation suggests that our mechanism for runtime fuzzer guidance, where the fuzzed code is instrumented with additional branches, is effective and could be leveraged to encode guidance from human users or static analyzers. Our results also show that the impact of fine-grained metrics over fuzzing performance is hard to predict before fuzzing and most of the time either neutral or negative. As a consequence, we do not recommend using them to guide fuzzers, except maybe in some possibly favorable circumstances yet to be investigated, like for limited parts of the code or to complement classical fuzzing campaigns.

Test Coverage in Microservice Systems: An Automated Approach to E2E and API Test Coverage Metrics

Test coverage is a critical aspect of the software development process, aiming for overall confidence in the product. When considering cloud-native systems, testing becomes complex, as it becomes necessary to deal with multiple distributed microservices that are developed by different teams and may change quite rapidly. In such a dynamic environment, it is important to track test coverage. This is especially relevant for end-to-end (E2E) and API testing, as these might be developed by teams distinct from microservice developers. Moreover, indirection exists in E2E, where the testers may see the user interface but not know how comprehensive the test suits are. To ensure confidence in health checks in the system, mechanisms and instruments are needed to indicate the test coverage level. Unfortunately, there is a lack of such mechanisms for cloud-native systems. This manuscript introduces test coverage metrics for evaluating the extent of E2E and API test suite coverage for microservice endpoints. It elaborates on automating the calculation of these metrics with access to microservice codebases and system testing traces, delves into the process, and offers feedback with a visual perspective, emphasizing test coverage across microservices. To demonstrate the viability of the proposed approach, we implement a proof-of-concept tool and perform a case study on a well-established system benchmark assessing existing E2E and API test suites with regard to test coverage using the proposed endpoint metrics. The results of endpoint coverage reflect the diverse perspectives of both testing approaches. API testing achieved 91.98% coverage in the benchmark, whereas E2E testing achieved 45.42%. Combining both coverage results yielded a slight increase to approximately 92.36%, attributed to a few endpoints tested exclusively through one testing approach, not covered by the other.

Performance evaluation of six mesh router replacement methods for wireless mesh networks: A comparison study for small and middle scale networks considering two islands distribution of mesh clients

In this paper, we present a hybrid intelligent simulation system for optimization of mesh routers in Wireless Mesh Networks (WMNs) called WMN-PSOHCDGA. We implemented six mesh router replacement methods: CM, RIWM, LDIWM, LDVM, RDVM and FC-RDVM and consider Two Islands distribution of mesh clients. We carry out a comparison study of these router replacement methods for small and middle scale WMNs. We assessed the performance by computer simulations. The simulation results show that six methods have a good performance for connectivity and coverage metrics, for both small and middle scale WMNs. However, they have different behavior for load balancing. For small scale WMNs, the load balancing of LDIWM, RIWM and FC-RDVM is better than CM, LDVM and RDVM. While, comparing LDIWM, RIWM and FC-RDVM, the LDIWM has better load balancing. We found that the load balancing for small scale WMNs is not good, because there is a concentration of mesh routers in some areas. For middle scale WMNs, the CM, LDIWM, LDVM and RDVM have not a good load balancing. While, the RIWM and FC-RDVM have better performance. Comparing RIWM and FC-RDVM, we found that the load balancing of FC-RDVM is better than RIWM.

Coverage-enhanced fault diagnosis for Deep Learning programs: A learning-based approach with hybrid metrics

Context:Given the data-driven paradigm inherent to Deep Learning (DL), it is inevitable that DL software will exhibit incorrect behavior in real-world applications. DL programs have been identified as a primary source of DL faults. To tackle this, researchers have devised a unique framework that approaches fault diagnosis as a learning task, which leverages runtime data as metrics to construct predictive models, enabling effective fault diagnosis. Object:In this paper, we aim to propose new metrics, especially from the coverage view, to enhance the performance of fault diagnosis models. Method:We combine coverage criteria and statistical operators to propose 80 coverage metrics, which summarize the trend of coverage values in the model training procedure. We construct hybrid prediction models by combining our new coverage metrics and existing runtime metrics under four widely used classifiers. Results:To examine whether adding our new coverage metrics performs well in DL program fault diagnosis, we conduct our experiments on six widely used datasets under four indicators (i.e., accuracy, F1 score, AUC, and MCC). Through the experiments, we observe that (a) coverage metrics are not redundant with respect to the original runtime metrics, and (b) adding extra coverage metrics can significantly enhance the performance of fault diagnosis models. Conclusions:Our study shows that our proposed coverage metrics are helpful in constructing effective fault diagnosis models for DL programs.

COSMIC: Molecular Conformation Space Modeling in Internal Coordinates with an Adversarial Framework.

The fast and accurate conformation space modeling is an essential part of computational approaches for solving ligand and structure-based drug discovery problems. Recent state-of-the-art diffusion models for molecular conformation generation show promising distribution coverage and physical plausibility metrics but suffer from a slow sampling procedure. We propose a novel adversarial generative framework, COSMIC, that shows comparable generative performance but provides a time-efficient sampling and training procedure. Given a molecular graph and random noise, the generator produces a conformation in two stages. First, it constructs a conformation in a rotation and translation invariant representation─internal coordinates. In the second step, the model predicts the distances between neighboring atoms and performs a few fast optimization steps to refine the initial conformation. The proposed model considers conformation energy, achieving comparable space coverage, and diversity metrics results.

A Novel Approach to Sensor Placement: Recursive Exhaustive Search and Stochastic Optimization Parameter Impact Analysis

This study presents a comprehensive approach for single sensor placement optimization in two-dimensional and three-dimensional spaces. A traditional exhaustive search technique and a novel method called recursive exhaustive search are used to place a sensor in a way that maximizes the area coverage metric. Exhaustive search provides a baseline by methodically evaluating all potential placements, while recursive exhaustive search innovates by segmenting the search process into more manageable, recursive steps. Our findings highlight the significant impact of two key parameters, the number of evaluations and the rasterization value, on the achieved coverage and computation time. The results show that the right choice of parameters can significantly reduce the computational effort without compromising the quality of the solution. This underlines the critical need for a balanced approach that considers both computational complexity and placement efficacy. We show that exhaustive search is not feasible for three-dimensional environment models and propose to establish a modified exhaustive search method as a ground truth for the single sensor placement problem. We then explore nature-inspired genetic algorithms and the impact of the number of evaluations of the optimization function for these algorithms on both accuracy and computational cost.

Abstract 318: Demystifying tumor heterogeneity with a fully automated, high-throughput single-cell DNA-Seq (scDNA-seq) workflow

Abstract Objective: Genetic heterogeneity is a key factor underlying tumor drug resistance and metastatic potential. Understanding this heterogeneity is therefore vital to improving both prognosis and treatment. Next-generation sequencing is a valuable tool for analyzing the genetic makeup of tumors. However, bulk sequencing methods lack the sensitivity to fully resolve tumor heterogeneity. While single cell methods provide a powerful approach to dissect such heterogeneity, to date, these methods have been limited in their throughput. To address this bottleneck, we have developed a fully automated workflow for generating scDNA-seq libraries based on PicoPLEX® whole genome amplification (WGA) technology. This high-throughput method, which has been optimized on our ICELL8® cx Single-Cell System, enables the generation of WGA libraries for &amp;gt;1,000 single cells within one day. Methods: We first sought to demonstrate the ability of this new high-throughput method to generate WGA libraries of comparable quality to the standard PicoPLEX workflow in terms of genome coverage, GC bias, and other typical quality metrics. We additionally assessed copy number variant (CNV) detection sensitivity using two cell lines with well-characterized small segmental CNVs: GM22601 (~25Mb deletion on chromosome 4) and GM05067 (~45Mb gain on chromosome 9). We also analyzed a lymphoblastoid line (K562) that carries a range of chromosomal aneuploidies. A total of 1,288 single-cell WGA libraries were generated and sequenced to a depth of 250K paired-end reads per cell. Data analysis was done using the Ginkgo CNV pipeline with an average bin size of 500 kb. As a final proof of principal, we also generated single cell data using tumor and adjacent normal tissue from two clear cell renal cell carcinoma (ccRCC) samples. Results: Libraries generated using our high-throughput workflow had a high mapping rate, with 94.1% of the reads being uniquely mapped. Additionally, the libraries were comparable to those generated with the standard PicoPLEX workflow in terms of coverage uniformity, GC bias and other metrics. Moreover, the segmental aneuploidies in both GM22601 and GM05067 were reliably detected in &amp;gt;90% of cells at a read depth as low as 250,000 reads per cell. Analysis of the ccRCC samples revealed subclonal heterogeneity with various CNVs common to ccRCC, including deletion of chr. 3p, amplification of chr. 5q, and duplication of chr. 2. Conclusion: By adapting PicoPLEX technology to high throughput using the ICELL8 cx Single-Cell System, we have obtained single-cell WGA libraries from up to 1,200 cells at once and enabled the reliable detection of CNVs and tumor subclones at a shallow sequencing depth. In addition, by leveraging the automated nanoliter-dispensing capabilities of ICELL8 cx system this method provides a significant reduction in reagent use and labor compared to plate-based methods. Citation Format: Xuan Li, Raymond Mendoza, Samantha Leong, Hima Anbunathan, Mike Covington, Mohammad Fallahi, Bryan Bell, Shuwen Chen, Yue Yun, Andrew Farmer. Demystifying tumor heterogeneity with a fully automated, high-throughput single-cell DNA-Seq (scDNA-seq) workflow [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 318.

On the suitability of checked coverage and genetic parameter tuning in test suite reduction

AbstractAs software projects evolve and grow in size and complexity, so do their test suites. Test suite reduction (TSR) aims at reducing the size of a test suite by removing redundant and obsolete test cases based on a coverage metric while preserving its fault detection capabilities. The contributions of this paper are twofold: (1) We examine a lesser‐known coverage criterion, that is, checked coverage. Checked coverage not only investigates if a part of the code was executed but also if it was checked by a test oracle. In an empirical evaluation, we performed TSR based on different reduction algorithms, coverage metrics, and open‐source Java projects with our own TSR tool to determine the most effective and efficient combination of metric and method. (2) Given the results of the first evaluation, we further investigate the potential of parameter optimization in regard to a genetic reduction algorithm. In particular, we focus on finding a general setting for the parameters crossover rate and mutation rate such that test suites can be reduced in a reasonable time while maintaining a high fault detection power.

An IMRT planning technique for treating whole breast or chest wall with regional lymph nodes on Halcyon and Ethos.

Field size limitations on Halcyon and Ethos treatment machines largely preclude use of the conventional monoisocentric three-field technique for breast/chest wall and regional lymph nodes. We present an alternative, IMRT-based planning approach that facilitates treatment on Halcyon and Ethos while preserving plan quality. Eight breast and regional node cases (four left-sided, four right-sided) were planned for an Ethos machine using a 15-17 field IMRT technique. Institutional plan quality metrics for CTV and PTV coverage and OAR sparing were assessed. Five plans (four right-sided, one left-sided) were also planned using a hybrid 3D multisocenter technique. CTV coverage and OAR sparing were compared to the IMRT plans. Eclipse scripting tools were developed to aid in beam placement and plan evaluation through a set of dosimetric scorecards, and both are shared publicly. On average, the IMRT plans achieved breast CTV and PTV coverage at 50Gy of 97.9% and 95.7%, respectively. Supraclavicular CTV and PTV coverages at 45Gy were 100% and 95.5%. Axillary lymph node CTV and PTV coverages at 45Gy were 100% and 97.1%, and IMN CTV coverage at 45Gy was 99.2%. Mean ipsilateral lung V20 Gy was 19.3%, and average mean heart dose was 1.6Gy for right-sided cases and 3.0Gy for left-sided. In comparison to the hybrid 3D plans, IMRT plans achieved higher breast and supraclavicular CTV coverage (99.9%vs. 98.6% and 99.9%vs. 93.4%), higher IMN coverage (99.6%vs. 78.2%), and lower ipsilateral lung V20 Gy (19.6%vs. 28.2%). Institutional plan quality benchmarks were achieved for all eight cases using the IMRT-based planning approach. The IMRT-based planning approach offered superior conformity and OAR sparing than a competing hybrid 3D approach.

Energy efficient clustering for dense wireless sensor network by applying Graph Neural Networks with coverage metrics

Wireless sensor networks (WSNs) have become increasingly important in recent years due to their ability to monitor and collect data in various environments. However, WSNs often face limitations in terms of their energy resources, making energy efficiency a critical concern when designing WSNs. Clustering and multihop routing are effective methods for maximizing the lifetime of WSNs. The Graph Neural Network (GNN) is an emerging architecture in neural networks that has recently gained attention for solving problems in various domains, including wireless networks. In this paper, we propose a method based on GNN to create static clusters of equal size. This approach aims to balance energy consumption among the nodes, which is an important sub-goal in designing an energy-efficient WSN clustering protocol. Additionally, we introduce a distributed cluster head selection scheme that operates independently within each cluster. Another crucial aspect of energy efficiency in WSNs is multi-hop routing, which helps avoid long-distance communication that consumes significant amounts of energy. Our proposed centralized routing protocol establishes exclusive routes to the base station for each cluster. This ensures even energy consumption for relay nodes and prevents the hotspot problem. To evaluate our proposed protocol, we conducted simulations comparing it with several state-of-the-art counterparts. The empirical results demonstrate that our approach outperforms other protocols in terms of lifetime and coverage metrics.

The Australian Eye and Ear Health Survey (AEEHS): Study protocol for a population-based cross-sectional study.

Vision and hearing impairments are highly prevalent and have a significant impact on physical, psychological and social wellbeing. There is a need for accurate, contemporary national data on the prevalence, risk factors and impacts of vision and hearing loss in Australian adults. The Australian Eye and Ear Health Survey (AEEHS) aims to determine the prevalence, risk factors and impacts of vision and hearing loss in both Aboriginal and Torres Strait Islander and non-Indigenous older adults. The AEEHS is a population-based cross-sectional survey which will include 5,000 participants (3250 non-Indigenous aged 50 years or older and 1750 Aboriginal and Torres Strait Islander people aged 40 years or older) from 30 sites covering urban and rural/regional geographic areas, selected using a multi-stage, random cluster sampling strategy. Questionnaires will be administered to collect data on socio-demographic, medical, ocular and ontological history. The testing battery includes assessment of blood pressure, blood sugar, anthropometry, visual acuity (presenting, unaided, pinhole and best-corrected), refraction, tonometry, slit lamp and dilated eye examination, ocular imaging including optical coherence tomography (OCT), OCT-angiography and retinal photography, and automated visual fields. Audiometry, tympanometry and video otoscopy will also be performed. The primary outcomes are age-standardised prevalence of cause-specific vision and hearing impairment. Secondary outcomes are prevalence of non-blinding eye diseases (including dry eye disease), patterns in health service utilisation, universal health coverage metrics, risk factors for vision and hearing impairment, and impact on quality of life.

Targeted amplification and genetic sequencing of the severe acute respiratory syndrome coronavirus 2 surface glycoprotein.

The COVID-19 pandemic was accompanied by an unprecedented surveillance effort. The resulting data were and will continue to be critical for surveillance and control of SARS-CoV-2. However, some genomic surveillance methods experienced challenges as the virus evolved, resulting in incomplete and poor quality data. Complete and quality coverage, especially of the S-gene, is important for supporting the selection of vaccine candidates. As such, we developed a robust method to target the S-gene for amplification and sequencing. By focusing on the S-gene and imposing strict coverage and quality metrics, we hope to increase the quality of surveillance data for this continually evolving gene. Our technique is currently being deployed globally to partner laboratories, and public health representatives from 79 countries have received hands-on training and support. Expanding access to quality surveillance methods will undoubtedly lead to earlier detection of novel variants and better inform vaccine strain selection.