Relative Coverage Research Articles

A tip of the iceberg: genome survey indicated a complex evolutionary history of Garuga Roxb. species

Background Garuga Roxb. is a genus endemic to southwest China and other tropical regions in Southeast Asia facing risk of extinction due to the loss of tropical forests and changes in land use. Conducting a genome survey of G. forrestii contribute to a deeper understanding and conservation of the genus.ResultsThis study utilized genome survey of G. forrestii generated approximately 54.56 GB of sequence data, with approximately 112 × coverage. K-mer analysis indicated a genome size of approximately 0.48 GB, smaller than 0.52GB estimated by flow cytometry. The heterozygosity is of about 0.54%, and a repeat rate of around 51.54%. All the shotgun data were assembled into 339,729 scaffolds, with an N50 of 17,344 bp. The average content of guanine and cytosine was approximately 35.16%. A total of 330,999 SSRs were detected, with mononucleotide repeats being the most abundant at 70.16%, followed by dinucleotide repeats at 20.40%. We conducted a preliminary ploidy assessment using Smudgeplot and observed a clear bimodal distribution in G. forrestii at 1/2 relative coverage depth and total coverage depth (2n), suggesting a potential diploid genome structure. A pseudo chromosome of G. forrestii and a gemone of Boswellia sacra were used as reference genome to perform a primer population resequencing analysis within three Garuga species. Principal component analysis (PCA) indicated three distinct groups, but genome wide phylogenetics represented conflicting both between the dataset of different reference genomes and between maternal and nuclear genome.ConclusionIn summary, the genome of G. forrestii is small, and the phylogenetic relationships within the Garuga genus are complex. The genetic data presented in this study holds significant value for comprehensive whole-genome analyses, the evaluation of population genetic diversity, investigations into adaptive evolution, the advancement of artificial breeding efforts, and the support of species conservation and restoration initiatives. Ultimately, this research contributes to reinforcing the conservation and management of natural ecosystems, promoting biodiversity conservation, and advancing sustainable development.

MVP: a modular viromics pipeline to identify, filter, cluster, annotate, and bin viruses from metagenomes.

While numerous computational frameworks and workflows are available for recovering prokaryote and eukaryote genomes from metagenome data, only a limited number of pipelines are designed specifically for viromics analysis. With many viromics tools developed in the last few years alone, it can be challenging for scientists with limited bioinformatics experience to easily recover, evaluate quality, annotate genes, dereplicate, assign taxonomy, and calculate relative abundance and coverage of viral genomes using state-of-the-art methods and standards. Here, we describe Modular Viromics Pipeline (MVP) v.1.0, a user-friendly pipeline written in Python and providing a simple framework to perform standard viromics analyses. MVP combines multiple tools to enable viral genome identification, characterization of genome quality, filtering, clustering, taxonomic and functional annotation, genome binning, and comprehensive summaries of results that can be used for downstream ecological analyses. Overall, MVP provides a standardized and reproducible pipeline for both extensive and robust characterization of viruses from large-scale sequencing data including metagenomes, metatranscriptomes, viromes, and isolate genomes. As a typical use case, we show how the entire MVP pipeline can be applied to a set of 20 metagenomes from wetland sediments using only 10 modules executed via command lines, leading to the identification of 11,656 viral contigs and 8,145 viral operational taxonomic units (vOTUs) displaying a clear beta-diversity pattern. Further, acting as a dynamic wrapper, MVP is designed to continuously incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field of viromics. MVP is available at https://gitlab.com/ccoclet/mvp and as versioned packages in PyPi and Conda.IMPORTANCEThe significance of our work lies in the development of Modular Viromics Pipeline (MVP), an integrated and user-friendly pipeline tailored exclusively for viromics analyses. MVP stands out due to its modular design, which ensures easy installation, execution, and integration of new tools and databases. By combining state-of-the-art tools such as geNomad and CheckV, MVP provides high-quality viral genome recovery and taxonomy and host assignment, and functional annotation, addressing the limitations of existing pipelines. MVP's ability to handle diverse sample types, including environmental, human microbiome, and plant-associated samples, makes it a versatile tool for the broader microbiome research community. By standardizing the analysis process and providing easily interpretable results, MVP enables researchers to perform comprehensive studies of viral communities, significantly advancing our understanding of viral ecology and its impact on various ecosystems.

HPV Vaccination Coverage in Brazil's State of Paraná: Spatial Distribution and Advances in Public Health.

Background/Objectives: To analyze the spatial distribution of HPV vaccination coverage in relation to sociodemographic variables in a state of Southern Brazil. Methods: This was an ecological, retrospective study with secondary data from the Department of Information Technology of the Unified Health System/Ministry of Health from 2015 to 2022. The cohort method was used to calculate vaccination coverage. Geographically weighted regression was used for the independent variables. Results: There was a 22.04% reduction in vaccination between the first and second doses. Coverage with the first dose of the vaccine reached 95.17% for the female population, 64.67% for the male population, and 79.57% for both sexes. In 50.62% of cities, coverage exceeded 90% for both sexes. In 80.45% of cities, the recommended coverage for females was achieved. The variable municipal performance was positively significant for the increase in vaccination coverage in 45.45% of the regions for girls, 18.18% for boys, and 36.36% for both sexes. The family health strategy variable was significant in 9.09% of the regions for girls and both sexes. The education variable showed an inverse significance for girls in 40.90%, for boys in 18.18%, and for both sexes in 36.36% of the regions. Conclusions: HPV vaccination declined between the first and second doses, with high first-dose coverage among females and moderate coverage among males. Municipal performance notably impacted coverage, particularly for girls. The family health strategy was relevant in specific regions, while educational factors had a variable effect. Addressing these variables may enhance vaccination coverage and minimize the gap between doses.

Population size, HIV prevalence, and antiretroviral therapy coverage among key populations in sub-Saharan Africa: collation and synthesis of survey data, 2010–23

Key population HIV programmes in sub-Saharan Africa require epidemiological information to ensure equitable and universal access to effective services. We aimed to consolidate and harmonise survey data among female sex workers, men who have sex with men, people who inject drugs, and transgender people to estimate key population size, HIV prevalence, and antiretroviral therapy (ART) coverage for countries in mainland sub-Saharan Africa. Key population size estimates, HIV prevalence, and ART coverage data from 39 sub-Saharan Africa countries between 2010 and 2023 were collated from existing databases and verified against source documents. We used Bayesian mixed-effects spatial regression to model urban key population size estimates as a proportion of the gender-matched, year-matched, and area-matched population aged 15-49 years. We modelled subnational key population HIV prevalence and ART coverage with age-matched, gender-matched, year-matched, and province-matched total population estimates as predictors. We extracted 2065 key population size data points, 1183 HIV prevalence data points, and 259 ART coverage data points. Across national urban populations, a median of 1·65% (IQR 1·35-1·91) of adult cisgender women were female sex workers, 0·89% (0·77-0·95) were men who have sex with men, 0·32% (0·31-0·34) were men who injected drugs, and 0·10% (0·06-0·12) were women who were transgender. HIV prevalence among key populations was, on average, four to six times higher than matched total population prevalence, and ART coverage was correlated with, but lower than, the total population ART coverage with wide heterogeneity in relative ART coverage across studies. Across sub-Saharan Africa, key populations were estimated as comprising 1·2% (95% credible interval 0·9-1·6) of the total population aged 15-49 years but 6·1% (4·5-8·2) of people living with HIV. Key populations in sub-Saharan Africa experience higher HIV prevalence and lower ART coverage, underscoring the need for focused prevention and treatment services. In 2024, limited data availability and heterogeneity constrain precise estimates for programming and monitoring trends. Strengthening key population surveys and routine data within national HIV strategic information systems would support more precise estimates. UNAIDS, Bill & Melinda Gates Foundation, and US National Institutes of Health.

Enhancing Anode Reversal Tolerance by Low-Carbon and Carbon-Free Anode Catalyst Layers for Proton Exchange Membrane Fuel Cells

Proton exchange membrane fuel cells (PEMFCs), as the main hydrogen utilization device, are considered a powerful tool for accelerating a carbon-neutral society. However, durability and cost are the two main obstacles to the commercialization of PEMFCs. Specifically, the anode reversal has been extensively studied as a severe issue that deteriorates the performance and durability of PEMFCs and leads to cell failure. Commercially, iridium oxide (IrOx) or iridium black is intensively utilized as oxygen evolution reaction catalysts to fabricate reversal tolerant anodes (RTAs) despite the slight performance loss. However, previous studies concentrated on investigating the origin or phenomena during the cell-reversal, the key factors and mechanisms affecting the anti-reversal performance, and the interaction among the anode catalyst layer components are still yet to be explored.Hence, we systematically studied the performance using three typical carbon supports with three metal contents and different metal deposition orders by experiments and model calculations. First, we synthesized Ir outside Ir/Pt/C and Pt outside Pt/It/C with uniform nanoparticle size and distribution and characterized them comprehensively. A new parameter, “relative metal coverage”, is put forward to evaluate and predict the anti-reversal ability, showing that higher relative metal coverage results in a longer reversal time and lower polarization performance degradation rate. In addition, Ir outside Ir/Pt/C shows better reversal tolerance than Pt outside Pt/It/C, which has been rarely mentioned in previous studies. Further, the experimental results show that even though the metal coverage is high, the performance degradation caused by carbon corrosion is still inevitable. On this basis, we synthesized a high specific surface area conductive oxide Ti4O7 as the anode catalyst support to eliminate the adverse effects of carbon corrosion. Previously, the initial performance of Ti4O7-supported catalyst was obstructed by the disadvantaged electrical conductivity of Ti4O7. We obtain a comparable polarization performance after optimizing the Ti4O7-supported anode catalyst layer parameters by shortening the electron transfer pathway and increasing metal coverage. Typically, the Ir@IrOx/Pt/Ti4O7-fabricated RTA with low Ir loading displays approximately ten times longer reversal time (6 hours) and two orders of magnitude lower degradation rate than a conventional carbon-supported counterpart. The degradation origin of the Ti4O7-supported anodes is also studied by postmortem characterizations, pointing to the Pt oxidation caused by the formation of TiOx thin layers on the Pt surface. These two works aim to promote the development of highly durable and reversal tolerance anode and provide a bright perspective for practical high-performance, low-degradation, and cost-friendly RTA fabrication.

Code-Aware Prompting: A Study of Coverage-Guided Test Generation in Regression Setting using LLM

Testing plays a pivotal role in ensuring software quality, yet conventional Search Based Software Testing (SBST) methods often struggle with complex software units, achieving suboptimal test coverage. Recent work using large language models (LLMs) for test generation have focused on improving generation quality through optimizing the test generation context and correcting errors in model outputs, but use fixed prompting strategies that prompt the model to generate tests without additional guidance. As a result LLM-generated testsuites still suffer from low coverage. In this paper, we present SymPrompt, a code-aware prompting strategy for LLMs in test generation. SymPrompt’s approach is based on recent work that demonstrates LLMs can solve more complex logical problems when prompted to reason about the problem in a multi-step fashion. We apply this methodology to test generation by deconstructing the testsuite generation process into a multi-stage sequence, each of which is driven by a specific prompt aligned with the execution paths of the method under test, and exposing relevant type and dependency focal context to the model. Our approach enables pretrained LLMs to generate more complete test cases without any additional training. We implement SymPrompt using the TreeSitter parsing framework and evaluate on a benchmark challenging methods from open source Python projects. SymPrompt enhances correct test generations by a factor of 5 and bolsters relative coverage by 26% for CodeGen2. Notably, when applied to GPT-4, SymPrompt improves coverage by over 2x compared to baseline prompting strategies.

Al-Sabah Newspaper’s Coverage of Saudi-Iranian Relations

Al-Sabah Newspaper’s Coverage of Saudi-Iranian Relations

Proposal of an environmental performance index to assess leather-manufacturing companies

Despite the global economic and environmental importance of the leather industry, the specialized literature still presents an important gap in analyzing the environmental performance of the leather manufacturing process from the waste generated. This paper aims to propose an unprecedented index (Index of Leather Manufacturing- ILM) to evaluate the environmental performance of leather manufacturing processes. The development of the index combines Life Cycle Assessment (LCA) and a Multicriteria Decision Method (MCDM) and is oriented to consider the impact of waste on environmental health. Its estimate is based on: the calculation of the environmental potential impact of the waste; the waste quantity produced by the company over a given period; the relative geographic coverage that each waste can achieve; the assessment of the appropriateness of the disposal of waste generated by the firm. The proposed index was tested on a large Brazilian leather manufacturer. The results showed that the waste management in the company is not perfect but is well rated, and residues with the greatest environmental impact are: wetblue shavings, wetblue sawdust, and gaseous emissions from beam house operation.

A Two‐stage Bayesian Small Area Estimation Approach for Proportions

SummaryWith the rise in popularity of digital Atlases to communicate spatial variation, there is an increasing need for robust small area estimates. However, current small area estimation methods suffer from various modelling problems when data are very sparse or when estimates are required for areas with very small populations. These issues are particularly heightened when modelling proportions. Additionally, recent work has shown significant benefits in modelling at both the individual and area levels. We propose a two‐stage Bayesian hierarchical small area estimation approach for proportions that can account for survey design, reduce direct estimate instability and generate prevalence estimates for small areas with no survey data. Using a simulation study, we show that, compared with existing Bayesian small area estimation methods, our approach can provide optimal predictive performance (Bayesian mean relative root mean squared error, mean absolute relative bias and coverage) of proportions under a variety of data conditions, including very sparse and unstable data. To assess the model in practice, we compare modelled estimates of current smoking prevalence for 1,630 small areas in Australia using the 2017–2018 National Health Survey data combined with 2016 census data.

A novel multiplex fluorescent-labeling method for the visualization of mixed-species biofilms in vitro.

In nature, bacteria usually exist as mixed-species biofilms, where they engage in a range of synergistic and antagonistic interactions that increase their resistance to environmental challenges. Biofilms are a major cause of persistent infections, and dispersal from initial foci can cause new infections at distal sites thus warranting further investigation. Studies of development and spatial interactions in mixed-species biofilms can be challenging due to difficulties in identifying the different bacterial species in situ. Here, we apply CellTrace dyes to studies of biofilm bacteria and present a novel application for multiplex labeling, allowing identification of different bacteria in mixed-species, in vitro biofilm models. Oral bacteria labeled with CellTrace dyes (far red, yellow, violet, and CFSE [green]) were used to create single- and mixed-species biofilms, which were analyzed with confocal spinning disk microscopy (CSDM). Biofilm supernatants were studied with flow cytometry (FC). Both Gram-positive and Gram-negative bacteria were well labeled and CSDM revealed biofilms with clear morphology and stable staining for up to 4 days. Analysis of CellTrace labeled cells in supernatants using FC showed differences in the biofilm dispersal between bacterial species. Multiplexing with different colored dyes allowed visualization of spatial relationships between bacteria in mixed-species biofilms and relative coverage by the different species was revealed through segmentation of the CSDM images. This novel application, thus, offers a powerful tool for studying structure and composition of mixed-species biofilms in vitro.IMPORTANCEAlthough most chronic infections are caused by mixed-species biofilms, much of our knowledge still comes from planktonic cultures of single bacterial species. Studies of formation and development of mixed-species biofilms are, therefore, required. This work describes a method applicable to labeling of bacteria for in vitro studies of biofilm structure and dispersal. Critically, labeled bacteria can be multiplexed for identification of different species in mixed-species biofilms using confocal spinning disk microscopy, facilitating investigation of biofilm development and spatial interactions under different environmental conditions. The study is an important step in increasing the tools available for such complex and challenging studies.

Classical Inference of a New PCI CNpmkc for Logistic-Exponential Process Distribution

The process capability index (PCI) has been developed as a useful instrument for measuring how well a product matches customer expectations while also assessing the performance of a process. We are aware that when the quality characteristics of the processes follow normal distribution, conventional PCIs produces superior results. However, these traditional indices could not yield reliable findings when assessing nonnormally distributed processes, which could make decision-making more difficult. In this paper, we take into account the [Formula: see text] process capacity index, which may be used for both normally distributed and nonnormally distributed processes. We have employed 10 traditional methods of estimation to estimate the PCI [Formula: see text] when the process has a logistic-exponential distribution, and the performances of these traditional estimates of the index [Formula: see text] are compared in terms of their mean squared errors through a simulation exercise. Then, we create five PCI [Formula: see text] bootstrap confidence intervals and contrast them based on their average widths and related coverage probabilities. In order to demonstrate the applicability of the suggested methods of estimation, two data sets pertaining to the electronic industries are re-analyzed.

Morphological and Molecular Identification of Ulva spp. (Ulvophyceae; Chlorophyta) from Algarrobo Bay, Chile: Understanding the Composition of Green Tides.

Green algae blooms of the genus Ulva are occurring globally and are primarily attributed to anthropogenic factors. At Los Tubos beach in Algarrobo Bay along the central Chilean coast, there have been blooms of these algae that persist almost year-round over the past 20 years, leading to environmental, economic, and social issues that affect the local government and communities. The objective of this study was to characterize the species that form these green tides based on a combination of ecological, morpho-anatomical, and molecular information. For this purpose, seasonal surveys of beached algal fronds were conducted between 2021 and 2022. Subsequently, the sampled algae were analyzed morphologically and phylogenetically using the molecular markers ITS1 and tufA, allowing for the identification of at least five taxa. Of these five taxa, three (U. stenophylloides, U. uncialis, U. australis) have laminar, foliose, and distromatic morphology, while the other two (U. compressa, U. aragoensis) have tubular, filamentous, and monostromatic fronds. Intertidal surveys showed that U. stenophylloides showed the highest relative coverage throughout the seasons and all intertidal levels, followed by U. uncialis. Therefore, we can establish that the green tides on the coast of Algarrobo in Chile are multispecific, with differences in relative abundance during different seasons and across the intertidal zone, opening opportunities for diverse future studies, ranging from ecology to algal biotechnology.

Shifts in dominance of benthic communities along a gradient of water temperature and turbidity in tropical coastal ecosystems.

Tropical coastal benthic communities will change in species composition and relative dominance due to global (e.g., increasing water temperature) and local (e.g., increasing terrestrial influence due to land-based activity) stressors. This study aimed to gain insight into possible trajectories of coastal benthic assemblages in Raja Ampat, Indonesia, by studying coral reefs at varying distances from human activities and marine lakes with high turbidity in three temperature categories (<31°C, 31-32°C, and >32°C). The benthic community diversity and relative coverage of major benthic groups were quantified via replicate photo transects. The composition of benthic assemblages varied significantly among the reef and marine lake habitats. The marine lakes <31°C contained hard coral, crustose coralline algae (CCA), and turf algae with coverages similar to those found in the coral reefs (17.4-18.8% hard coral, 3.5-26.3% CCA, and 15-15.5% turf algae, respectively), while the higher temperature marine lakes (31-32°C and >32°C) did not harbor hard coral or CCA. Benthic composition in the reefs was significantly influenced by geographic distance among sites but not by human activity or depth. Benthic composition in the marine lakes appeared to be structured by temperature, salinity, and degree of connection to the adjacent sea. Our results suggest that beyond a certain temperature (>31°C), benthic communities shift away from coral dominance, but new outcomes of assemblages can be highly distinct, with a possible varied dominance of macroalgae, benthic cyanobacterial mats, or filter feeders such as bivalves and tubeworms. This study illustrates the possible use of marine lake model systems to gain insight into shifts in the benthic community structure of tropical coastal ecosystems if hard corals are no longer dominant.

Seasonality and regional disparity in attention to homelessness in UK newspapers between 2001 and 2020

ABSTRACT This research examined the amount of media attention to homelessness in 11 UK newspapers with two main research questions: (1) Does the media coverage exhibit seasonal cyclical patterns in their attention to homelessness? (2) Do England's nine regions differ in the attention they receive after adjusting for their relative homelessness levels? Time series of all 4105 news reports with the keyword “homeless*” in their headline between 2001 and 2020 were tested for meteorological and monthly seasonal effects, revealing significantly greater attention in winter but decreased coverage in spring and summer. Contrary to expectation, further analysis suggested that the increased winter coverage was not related to the colder months but to the Christmas period. Regarding regional differences, London received the highest mentions in the news but after adjusting for the relative burden of homelessness, the North West emerged as receiving the most relative coverage. The East Midlands and East of England had the lowest adjusted attention scores. As suggested by the public arenas model, the findings of this research suggest that media focus on social problems like homelessness be culturally patterned with key events like Christmas shaping issue-prominence. Similarly, regional imbalances in attention may reflect a systemic bias in news reporting.

Statistical Inferences for Missing Response Problems Based on Modified Empirical Likelihood.

In this paper, we advance the application of empirical likelihood (EL) for missing response problems. Inspired by remedies for the shortcomings of EL for parameter hypothesis testing, we modify the EL approach used for statistical inference on the mean response when the response is subject to missing behavior. We propose consistent mean estimators, and associated confidence intervals. We extend the approach to estimate the average treatment effect in causal inference settings. We detail the analogous estimators for average treatment effect, prove their consistency, and example their use in estimating the average effect of smoking on renal function of the patients with atherosclerotic renal-artery stenosis and elevated blood pressure, chronic kidney disease, or both. Our proposed estimators outperform the historical mean estimators under missing responses and causal inference settings in terms of simulated relative RMSE and coverage probability on average.

Impact of alloying on electrocatalytic hydrogenation of benzaldehyde over sputtered NiCu film catalysts

In this work, we prepare a series of NiCu alloy film catalysts with different contents by magnetron sputtering to investigate the synergistic effect between Ni and Cu in electrocatalytic hydrogenation of benzaldehyde. The results show that alloying of Ni and Cu can significantly enhance the electrocatalytic activity of benzaldehyde hydrogenation. With the increase of Ni content, the conversion rate of benzaldehyde increases two to three orders of magnitude, and the Faradaic efficiency of the Ni-rich catalysts is higher than that of the Cu-rich catalysts. In addition, the alloy composition affects the relative coverages of H and benzaldehyde, leading to different reaction orders in benzaldehyde. The boosted electrocatalytic activity with the increase of Ni content is in accordance with the electrochemical impedance spectroscopy that increasing Ni content can decrease the charge transfer resistance of the alloy catalysts.

Evaluating the effectiveness of functional decomposition in early-stage design: development and application of problem space exploration metrics

This paper aims to explore metrics for evaluating the effectiveness of functional decomposition methods regarding problem space exploration at the early design stage. Functional decomposition involves breaking down the main purpose of a complex problem or system into a set of more manageable sub-functions, leading to a clearer understanding of the problem space and its various aspects. While various metrics have been used to evaluate functional decomposition outcomes, little literature has focused on assessing its effectiveness in problem space exploration. To address the gap, this research introduces three metrics for problem space evaluation defined by functional models: quantity of unique functions (M1), breadth and depth of the hierarchical structure (M2), and relative semantic coverage ratio of the problem space (M3). An example study is conducted to illustrate the evaluation process, comparing functional analysis with and without explicit human-centric considerations using a power screwdriver as a case product. The analysis in the example study reveals that the breadth of the hierarchical structure (part of M2) is marginally larger in the condition with explicit human-centric considerations (Condition A) compared to the condition without such considerations (Condition B). However, no significant differences are observed in terms of other metrics. The qualitative analysis based on semantic comparisons suggests that Condition A facilitates participants in generating a diverse set of functions supporting user safety requirements more effectively than Condition B. Overall, the example study demonstrates the evaluation process for each metric and discusses their nuances and limitations. By proposing these metrics, this research contributes to benchmarking and evaluating the effectiveness of different methods in promoting functional analysis in engineering design. The metrics provide valuable insights into problem space exploration, offering designers a better understanding of the efficacy of their functional decomposition methods in early design stages. This, in turn, fosters more informed decision-making and contributes to the advancement of functional analysis methodologies in engineering design practices.

A multi-step attack path prediction method for oil & gas intelligence pipeline cyber physics system based on CPNE

Owing to the integration of the 5th Generation Mobile Communication Technology (5 G) and industrial internet construction, Oil & Gas intelligent pipeline network has witnessed accelerated construction and development. The Oil & Gas intelligent pipeline network proposed by China National Petroleum Corporation exhibits the advantages such as high integration, data unification, and intelligent decision-making, which helps to greatly reduce manual management costs, optimize risk decision-making by managers, and avoid risks such as human misuse. However, the highly integrated Cyber-physical system of the Internet and the Internet of Things faces various cyber security threats, and traditional intrusion detection systems exhibit drawbacks in the face of complex cyber-attack behaviors. In response to incomplete multi-step attack path prediction in traditional prediction methods, this paper proposes a multi-step attack path prediction method for Oil & Gas intelligence pipeline cyber physics system (CPS) based on Colored Petri nets and mixed strategy Nash equilibrium (CPNE). Taking into account the importance of assets and the dependency relationships between assets within the cyber-physical system of Oil & Gas intelligent pipeline network, CPNE constructs a node dependency model for the cyber-physical system of Oil & Gas intelligent pipeline network, simulates multi-step attack and defense games, calculates the probability of node failure triggering and transfer using mixed strategy Nash equilibrium, and extracts the risk of multi-step attack paths for different attack strategies. With the filtration and separation unit of a certain natural gas station as an example, this paper establishes a CPNE model to rank the risk of the first multi-step attack single attack source path and predict the risk of subsequent multi-step attack source paths, thus predicting multi-step attack paths. Taking three rounds of attacks as an example, compared to the paths predicted by traditional probability attack graph models, the proposed CPNE model is more advantageous in multi attack source path prediction, with a 2.33-fold increase in the relative coverage rate of path predictions.

Abstract 3954: Increased mosaic chromosomal alterations among survivors of childhood acute myeloid leukemia

Abstract Background: We previously characterized point mutations of clonal hematopoiesis (CH) including single nucleotide variants or small insertion/deletions residing in a set of cancer-related genes (e.g., DNMT3A and TET2), and found that CH point mutations occurred at a much higher prevalence in childhood cancer survivors (CCS) than in age-sex-race matched community controls. However, mosaic chromosomal alterations (mCAs), another form of CH with megabase-scale structural alterations (copy gain, copy loss and copy-neutral loss of heterozygosity), have not been examined. Methods: Detection of mCAs was performed on whole-genome sequencing (WGS, 30X) data from 5-year CCS with the Mosaic Chromosomal Alterations (MoChA) caller which utilizes phased genotypes, coverage, and B allele frequency (BAF) at heterozygous sites (HETS). We implemented the pipeline established by the TOPMed consortium to detect low mutant cell fractions (CF ≥1%). Variant filtration criteria: minor allele frequency &amp;lt;1%, the read depth of either allele &amp;lt;5, and only one variant retained in each 1Kb window. Called mCA filtration criteria: &amp;lt;100 HETS; lod score &amp;lt;5; relative coverage &amp;gt;2.9 or BAF deviation &amp;gt;0.16 and relative coverage &amp;gt;2.5. Autosomal mCAs (CF ≥1%, HETS &amp;gt;100 and length &amp;gt;1Mb) retained. Multivariable Cox models evaluated the association between mCAs and specific late-effects including all-cause mortality, obesity, myocardial infarction (MI) or cardiomyopathy (CMP); multivariable logistic models evaluated the association between mCAs and childhood cancer diagnoses. All models were adjusted for age, sex and therapeutic exposures. Results: In our evaluation of 4,330 CCS (median age at blood draw: 26.8 years) and 433 controls (33.6 years) with WGS data from the St. Jude Lifetime Cohort, the prevalence of mCAs was slightly higher among CCS than community controls (4.83% vs. 4.16%). Among CCS, mCAs were associated with therapeutic exposures (alkylating agents: odds ratio [OR]=1.48, 95% CI=1.09-2.00; antimetabolites: 1.53, 1.02-2.31; corticosteroids: 0.60, 0.40-0.91). In contrast to recent mCA studies in the general population, we observed no co-occurrence with CH point mutations, and no associations with late-effects including all-cause mortality, obesity, MI or CMP. Interestingly, mCAs were significantly associated with the primary diagnosis of acute myeloid leukemia (AML, OR=2.86, 95% CI=1.63-5.01). Furthermore, this finding was replicated in 2898 5-year survivors from the Childhood Cancer Survivor Study (OR=3.77, 95% CI=1.94-7.33). Conclusion: mCAs were not associated with age-related morbidities and mortality among CCS. However, we identified a striking association between mCAs and previous diagnoses of AML independent of treatment exposures. If validated among AML patients at the time of diagnosis, mCAs, which reflect the underlying genome instability, may serve as a diagnostic biomarker for AML. Citation Format: Xijun Zhang, Na Qin, Qian Dong, Cheng Chen, John Easton, Heather Mulder, Xiang Chen, Kyla Shelton, Cindy Im, Yutaka Yasui, Greg Armstrong, Kirsten K. Ness, Melissa M. Hudson, Paul Auer, Jinghui Zhang, Zhaoming Wang. Increased mosaic chromosomal alterations among survivors of childhood acute myeloid leukemia [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 3954.

Measuring Monetary Policy Shocks in Emerging Economies: Evidence from India

AbstractIn this paper, we provide a template for constructing monetary policy shocks for emerging economies. Our approach synthesizes financial data with a narrative analysis of central bank communication and related media coverage. We create a publicly available time‐series database of policy dates and shocks for the Reserve Bank of India (RBI). Our shocks suggest that financial markets infer information about the future path of policy rate from RBI communication. Bond and stock markets react strongly to these monetary shocks but exhibit heterogeneity across governor regimes. Finally, we use the shocks as external instruments to identify the impact on macro‐economic variables.