Vulnerability Characteristics Research Articles

Study on the Anti-Progressive Collapse Behavior of Steel Frame Structures under Close-Range Blast Loading

The steel frame structure plays an important role in strategic deployments and is widely used in heavy machinery, metallurgy, military, and other important industries. To study the impact of explosive loads on the anti-progressive collapse performance of steel structures, this paper proposes to establish the vulnerability characteristics of steel frame structures and provides a method for calculating vulnerability characterization indicators. A finite element model is used to analyze the dynamic response of steel frame structures under the action of close-range explosive loads, and factors influencing the anti-progressive collapse of steel frame structures are proposed, including the number of stories and diagonal bracing. A comparison is made between the various column types of steel structures under explosive loads, such as corner columns, long-edge middle columns, short-edge middle columns, inner columns, also in various coupling conditions. The results show that the progressive collapse of steel frame structures is greatly influenced by the position of the explosion and less affected by the amount of explosive material. The simultaneous failure of corner columns and long-edge middle columns is more likely to cause overall structural failure. The addition of diagonal bracing significantly improves the anti-progressive collapse ability and prevents the lateral displacement of steel frame structures; increasing the number of stories provides more alternative load transfer paths for steel frame structures, thereby preventing their collapse.

A Developmental Perspective on Early and Current Motor Abnormalities and Psychotic-Like Symptoms.

Psychotic-like experiences (PLEs) are prevalent in the general population and, because they represent a lower end of the psychosis vulnerability spectrum, may be useful in informing mechanistic understanding. Although it is well-understood that motor signs characterize formal psychotic disorders, the developmental trajectory of these features and their relationships with PLEs are less well-understood. Data from 7559 adolescents and young adults (age 11-21) in the Philadelphia Neurodevelopmental Cohort were used to investigate whether early-life milestone-attainment delays relate to current adolescent sensorimotor functioning and positive and negative PLEs. Current sensorimotor functioning was assessed using the Computerized Finger Tapping task (assessing motor slowing) and Mouse Practice task (assessing sensorimotor planning). Early developmental abnormalities were related to current adolescent-aged motor slowing (t(7415.3) = -7.74, corrected-P < .001) and impaired sensorimotor planning (t(7502.5) = 5.57, corrected-P < .001). There was a significant interaction between developmental delays and current sensorimotor functioning on positive and negative PLEs (t = 1.67-4.51), such that individuals with early developmental delays had a stronger positive relationship between sensorimotor dysfunction and PLEs. Importantly, interaction models were significantly better at explaining current PLEs than those treating early and current sensorimotor dysfunction independently (χ2 = 4.89-20.34). These findings suggest a relationship between early developmental delays and current sensorimotor functioning in psychosis proneness and inform an understanding of heterotypic continuity as well as a neurodevelopmental perspective of motor circuits. Furthermore, results indicate that motor signs are a clear factor in the psychosis continuum, suggesting that they may represent a core feature of psychosis vulnerability.

Limited progress in global reduction of vulnerability to flood impacts over the past two decades

Global flood impacts have risen in recent decades. While increasing exposure was the dominant driver of surging impacts, counteracting vulnerability reductions have been detected, but were too weak to reverse this trend. To assess the ongoing progress on vulnerability reduction, we combine a recently available dataset of flooded areas derived from satellite imagery for 913 events with four global disaster databases and socio-economic data. Event-specific flood vulnerabilities for assets, fatalities and displacements reveal a lack of progress in reducing global flood vulnerability from 2000—2018. We examine the relationship between vulnerabilities and human development, inequality, flood exposure and local structural characteristics. We find that vulnerability levels are significantly lower in areas with good structural characteristics and significantly higher in low developed areas. However, socio-economic development was insufficient to reduce vulnerabilities over the study period. Nevertheless, the strong correlation between vulnerability and structural characteristics suggests further potential for adaptation through vulnerability reduction.

A machine learning approach to evaluate coastal risks related to extreme weather events in the Veneto region (Italy)

A significant and unprecedented increase of temperature has been recorded worldwide during the last decades, leading to the occurrence of numerous extreme events. Coastal areas, with their high population density, interconnected economic activities, fragile ecosystems, are particularly vulnerable to climate change impacts. These impacts can be intensified by the interactions of multiple hazards which operate at different spatio-temporal scales and affect exposure and vulnerability patterns. An integrated approach is here proposed to assess the relationship between risk factors and to evaluate the multiplicity of impacts that may affect the coastline. A new path to tackle these multi-risk events is offered by ML algorithms to effectively handle vast amounts of heterogenous data, and model complex non-linear relationships between multiple factors and feedback mechanisms. To assess impacts caused by extreme events (storm surges, extreme precipitation, wind events) in the Veneto coastal municipalities, a ML approach was developed to understand connections between atmospheric and marine hazards and impacts recorded by the Veneto region emergency archive during the 2009–2019 timeframe, identifying the most influencing factors triggering multiple risks. Additionally, the coastal municipalities were clustered considering the intrinsic relationships between impact occurrences, exposure and vulnerability features. Several algorithms were compared to estimate daily risk of impacts to occur providing hazards, exposure and vulnerability information. The MLP algorithm showed satisfactory performances (weighted-F1-score of 0.94) to estimate the relative importance of input features. The proposed algorithm was designed as support tool to increase the understanding of impacts’ occurrence in coastal areas, thus helping the adaptation planning process.

Congenital Syphilis Prevention Challenges, Pacific Coast of Colombia, 2018-2022.

High incidences of congenital syphilis have been reported in areas along the Pacific coast of Colombia. In this retrospective study, conducted during 2018-2022 at a public hospital in Buenaventura, Colombia, we analyzed data from 3,378 pregnant women. The opportunity to prevent congenital syphilis was missed in 53.1% of mothers because of the lack of syphilis screening. Characteristics of higher maternal social vulnerability and late access to prenatal care decreased the probability of having >1 syphilis screening test, thereby increasing the probability of having newborns with congenital syphilis. In addition, the opportunity to prevent congenital syphilis was missed in 41.5% of patients with syphilis because of the lack of treatment, which also increased the probability of having newborns with congenital syphilis. We demonstrate the urgent need to improve screening and treatment capabilities for maternal syphilis, particularly among pregnant women who are more socially vulnerable.

Provincial-scale assessment of vulnerability and resilience to drought in China

Frequent droughts have caused severe disaster losses in China. Such events can be minimized by enhancing the country's resilience and reducing its vulnerability, where this can ensure socioeconomic stability and sustainable development. Evaluating the vulnerability and resilience to drought is thus crucial for effectively managing the risk of disasters and promoting sustainable socioeconomic development. In this study, we constructed a comprehensive framework to assess the spatiotemporal characteristics of China's vulnerability and resilience to drought at the provincial scale from an input-output perspective by using the Super-Efficiency Data Envelopment Analysis (DEA) model and the Super-Efficiency Slacks-Based Measure DEA (SBM-DEA) model. This study focused on drought drivers, the disaster-forming environment, drought bearers, disaster intensity, and recovery. The results showed that the vulnerability to drought of 42 % of China's provinces decreased from 2010 to 2022, that of only 29 % of the provinces increased, while the status of a majority of provinces improved in general. The center of gravity of the vulnerability to drought moved toward the southwest over time and a spatial clustering of vulnerability was observed, with High-High clusters moving from the north to the south. Moreover, the resilience to drought declined in 36 % of provinces and increased in only 20 %, reflecting poor resilience overall. The center of gravity of China's overall resilience to drought moved northward, with a relatively stable spatial pattern and prominent clusters of Low-Low resilience indicating a pressing need for improvement. Areas with high vulnerability and low resilience were concentrated in inland western and eastern regions, and this highlights the importance of drought prevention and mitigation in provinces like Xinjiang, Inner Mongolia, Jiangxi, and Fujian. The findings here provide valuable insights for mitigating the risk of drought and promoting sustainable socioeconomic development.

Epidemiological features of depression and anxiety among homeless adults with healthcare access problems in London, UK: descriptive cross-sectional analysis.

In England in 2021, an estimated 274 000 people were homeless on a given night. It has long been recognised that physical and mental health of people who are homeless is poorer than for people who are housed. There are few peer-reviewed studies to inform health and social care for depression or anxiety among homeless adults in this setting. To measure the symptoms of depression and anxiety among adults who are homeless and who have difficulty accessing healthcare, and to describe distribution of symptoms across sociodemographic, social vulnerability and health-related characteristics. We completed structured questionnaires with 311 adults who were homeless and who had difficulty accessing healthcare in London, UK, between August and December 2021. We measured anxiety and depression symptoms using the 4-item Patient Health Questionnaire (PHQ-4) score. We compared median PHQ-4 scores across strata of the sociodemographic, social vulnerability and health-related characteristics, and tested for associations using the Kruskal-Wallis test. The median PHQ-4 score was 8 out of 12, and 40.2% had scores suggesting high clinical need. Although PHQ-4 scores were consistently high across a range of socioeconomic, social vulnerability and health-related characteristics, they were positively associated with: young age; food insecurity; recent and historic abuse; joint, bone or muscle problems; and frequency of marijuana use. The most common (60%) barrier to accessing healthcare related to transportation. Adults who are homeless and have difficulty accessing healthcare have high levels of depression and anxiety symptoms. Our findings support consideration of population-level, multisectoral intervention.

Layered plaque is associated with high levels of vascular inflammation and vulnerability in patients with stable angina pectoris.

Layered plaque, a signature of previous plaque destabilization and healing, is a known predictor for rapid plaque progression; however, the mechanism of which is unknown. The aim of the current study was to compare the level of vascular inflammation and plaque vulnerability in layered plaques to investigate possible mechanisms of rapid plaque progression. This is a retrospective, observational, single-center cohort study. Patients who underwent both coronary computed tomography angiography (CTA) and optical coherence tomography (OCT) for stable angina pectoris (SAP) were selected. Plaques were defined as any tissue (noncalcified, calcified, or mixed) within or adjacent to the lumen. Perivascular inflammation was measured by pericoronary adipose tissue (PCAT) attenuation at the plaque levels on CTA. Features of plaque vulnerability were assessed by OCT. Layered plaques were defined as plaques presenting one or more layers of different optical densities and a clear demarcation from underlying components on OCT. A total of 475 plaques from 195 patients who presented with SAP were included. Layered plaques (n = 241), compared with non-layered plaques (n = 234), had a higher level of vascular inflammation (-71.47 ± 10.74 HU vs. -73.69 ± 10.91 HU, P = 0.026) as well as a higher prevalence of the OCT features of plaque vulnerability, including lipid-rich plaque (83.8% vs. 66.7%, P < 0.001), thin-cap fibroatheroma (26.1% vs. 17.5%, P = 0.026), microvessels (61.8% vs. 34.6%, P < 0.001), and cholesterol crystals (38.6% vs. 25.6%, P = 0.003). Layered plaque was associated with a higher level of vascular inflammation and a higher prevalence of plaque vulnerability, which might play an important role in rapid plaque progression.Clinical trial registration: https://classic.clinicaltrials.gov/ct2/show/NCT04523194 .

Predicting the distribution of megabenthic communities on deep-water seamounts with cobalt-rich crusts in the Magellan Seamount Chain in the northwestern Pacific ocean

Deep-water seamount ecosystems are sensitive to human activities and have a slow recovery rate after being disturbed. Bottom trawling and potential deep-sea mineral extraction could severely damage benthic communities on seamounts and seriously impact deep-sea ecosystems. Inadequate knowledge of the distribution of megabenthos on seamounts or their community structure hinders deep-sea conservation and management. In this study, based on a multidisciplinary dataset generated from recordings taken by human-occupied vehicle (HOV) and remotely operated vehicles (ROVs) along transects and environmental variables, a range of megabenthic morphotaxa were observed on two adjacent deep-water seamounts and predicted using species distribution models (SDMs). Accordingly, based on the predicted distribution of each morphotaxon, five distinct communities were identified through cluster analysis. The results of SDMs showed that environmental variables varyingly impacted the distribution of different morphotaxa, among which the average velocity and eastness direction of near-bottom currents, bathymetric position index (BPI), and backscatter intensity exerted the most significant influence on megabenthic distribution patterns. The distribution of five distinct communities showed a similarity of community composition on the two deep-water seamounts, suggesting a potential connectivity between the two seamounts. The distribution of communities revealed the spatial characteristics of vulnerability of deep-water seamounts at the community level, which could provide a direct basis for marine spatial planning of deep-sea ecosystems.

Spatial enrichment and genomic analyses reveal the link of NOMO1 with amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a severe motor neuron disease with uncertain genetic predisposition in most sporadic cases. Spatial architecture of cell types and gene expression is the basis of cell-cell interactions, biological function and disease pathology, but is not well investigated in human motor cortex, a key ALS relevant brain region. Recent studies indicated single nucleus transcriptomic features of motor neuron vulnerability in ALS motor cortex. However, it remains largely unclear what is the brain regional vulnerability of ALS-associated genes, and what is the genetic link between region-specific genes and ALS risk. Here, we developed an entropy-weighted differential gene expression matrix-based tool (SpatialE) to identify the spatial enrichment of gene sets in spatial transcriptomics (ST). We benchmarked SpatialE against another enrichment tool (Multimodal Intersection Analysis, MIA) using ST data from both human and mouse brain tissues. To investigate regional vulnerability, we analyzed three human motor cortex and two dorsolateral prefrontal cortex tissues for spatial enrichment of ALS-associated genes. We also used Cell2location to estimate the abundance of cell types in ALS-related cortex layers. To dissect the link of regionally expressed genes and ALS risk, we performed burden analyses of rare loss-of-function (LOF) variants detected by whole-genome sequencing in ALS patients and controls, and then analyzed differential gene expression in the TargetALS RNA-seq dataset. SpatialE showed more accurate and specific spatial enrichment of regional cell type markers than MIA in both mouse brain and human dorsolateral prefrontal cortex. Spatial transcriptomic analyses of human motor cortex showed heterogenous cell types and spatial gene expression profiles. We found that 260 manually curated ALS-associated genes are significantly enriched in layer 5 (L5) motor cortex, with abundant expression of upper motor neurons and L5 excitatory neurons. Burden analyses of rare LOF variants in L5-associated genes nominated NOMO1 as a novel ALS-associated gene in a combined sample set of 6,814 ALS patients and 3,324 controls (P = 0.029). Gene expression analyses in central nervous system tissues revealed down-regulation of NOMO1 in ALS, which is consistent with a LOF disease mechanism. In conclusion, our integrated ST and genomic analyses identified regional brain vulnerability in ALS and the association of a L5 gene (NOMO1) with ALS risk.

Impact of endothelial shear stress on coronary atherosclerotic plaque progression and composition: A meta-analysis and systematic review

Background and aimsIntracoronary pressure gradients and translesional flow patterns have been correlated with coronary plaque progression and lesion destabilization. In this study, we aimed to determine the relationship between endothelial shear stress and plaque progression and to evaluate the effect of shear forces on coronary plaque features. MethodsA systematic review was conducted in medical on-line databases. Selected were studies including human participants who underwent coronary anatomy assessment with computational fluid dynamics (CFD)-based wall shear stress (WSS) calculation at baseline with anatomical evaluation at follow-up. A total of six studies were included for data extraction and analysis. ResultsThe meta-analysis encompassed 31′385 arterial segments from 136 patients. Lower translesional WSS values were significantly associated with a reduction in lumen area (mean difference −0.88, 95% CI −1.13 to −0.62), an increase in plaque burden (mean difference 4.32, 95% CI 1.65 to 6.99), and an increase in necrotic core area (mean difference 0.02, 95% CI 0.02 to 0.03) at follow-up imaging. Elevated WSS values were associated with an increase in lumen area (mean difference 0.78, 95% CI 0.34 to 1.21) and a reduction in both fibrofatty (mean difference −0.02, 95% CI −0.03 to −0.01) and fibrous plaque areas (mean difference −0.03, 95% CI −0.03 to −0.03). ConclusionThis meta-analysis shows that WSS parameters were related to vulnerable plaque features at follow-up. These results emphasize the impact of endothelial shear forces on coronary plaque growth and composition. Future studies are warranted to evaluate the role of WSS in guiding clinical decision-making.

Multiple techniques to evaluate the relationship between carotid artery plaque and acute stroke.

To evaluate the important characteristics of the plaque vulnerability using multimodal ultrasound imaging methods (2D, contrast-enhanced ultrasound, and elastography), and to explore the relationship between plaque and acute stroke. A total of 244 patients with carotid plaque were enrolled, including 104 patients with acute stroke ipsilateral to the plaque as the case group and 140 patients as the control group. All patients underwent conventional carotid ultrasound, contrast-enhanced ultrasound (CEUS) and elastography (SWE). The results of each examination were compared and analyzed, and the relationship between the results and the occurrence of stroke was discussed. In the acute stroke group, the men, with a history of alcohol consumption the direction of contrast media diffusion was higher than that in the control group, but the plaque gray value (GSM), maximum, average and minimum Young's elastic modulus imaging values (YM) were slightly lower than those in the control group (P < 0.05). Logistic regression analysis showed that waist to body ratio (WHtR), GSM, YM, neovascularization density and contrast diffusion direction were independent risk factors for predicting acute ischemic stroke. The influence degree of each factor from strong to weak was waist to body ratio, neovascularity density, GSM and YM, respectively. The area under the curve (AUC) for the diagnosis of acute ischemic stroke by regression model was 0.746. The combination of multiple ultrasound techniques to evaluate the vulnerability of carotid plaque and predict the occurrence of acute stroke provides valuable information for clinical decision making.

The dynamic connectedness between renewable energy market and environmental protection industry based on time and frequency perspective

Renewable energy promotes the sustainable development of the environmental protection industry (EPI) and the economy. Meanwhile, the EPI provides various technologies and services for renewable energy, which is an important guarantee for the renewable energy market. This paper combines the DY and BK spillover indexes to discuss the cross-market risk contagion effect between EPI and renewable energy market from the perspective of time-frequency spillover. The results show that: (1) The spillover effect between the EPI and renewable energy market is cyclical from the perspective of frequency domain, and the total market spillover is dominated by high-frequency spillover. And the risk contagion has the characteristics of time-varying and vulnerability from the perspective of time domain. (2) The net spillover effect shows that the EPI plays the role of a net exporter in different frequency domains, the solar energy and the hydro market play more roles as risk receivers, and the role of the wind energy market is frequently converted. (3) The risk spillover network structure shows that EPI acts as an important node in the cross-market risk contagion path. Driven by EPI, the short-term risk transmission follows the path of “EPI-biomass energy-other four renewable energy markets”, and the medium and long-term follows the path of “EPI-wind energy-other four renewable energy markets”.

The vulnerability assessment and obstacle factor analysis of urban agglomeration along the Yellow River in China from the perspective of production-living-ecological space.

Urban agglomerations are sophisticated territorial systems at the mature stage of city development that are concentrated areas of production and economic activity. Therefore, the study of vulnerability from the perspective of production-living-ecological space is crucial for the sustainable development of the Yellow River Basin and global urban agglomerations. The relationship between productivity, living conditions, and ecological spatial quality is fully considered in this research. By constructing a vulnerability evaluation index system based on the perspectives of production, ecology, and living space, and adopting the entropy value method, comprehensive vulnerability index model, and obstacle factor diagnostic model, the study comprehensively assesses the vulnerability of the urban agglomerations along the Yellow River from 2001 to 2020. The results reveal that the spatial differentiation characteristics of urban agglomeration vulnerability are significant. A clear three-level gradient distribution of high, medium, and low degrees is seen in the overall vulnerability; these correspond to the lower, middle, and upper reaches of the Yellow River Basin, respectively. The percentage of cities with higher and moderate levels of vulnerability did not vary from 2001 to 2020, while the percentage of cities with high levels of vulnerability did. The four dimensions of economic development, leisure and tourism, resource availability, and ecological pressure are the primary determinants of the urban agglomeration's vulnerability along the Yellow River. And the vulnerability factors of various urban agglomerations showed a significant evolutionary trend; the obstacle degree values have declined, and the importance of tourism and leisure functions has gradually increased. Based on the above conclusions, we propose several suggestions to enhance the quality of urban development along the Yellow River urban agglomeration. Including formulating a three-level development strategy, paying attention to ecological and environmental protection, developing domestic and foreign trade, and properly planning and managing the tourism industry.

Study on bullet impact performance of the HATO-based explosives

In order to study the vulnerability of 1,1'-dihydroxy-5,5'-bistetrazole dihydroxylamine salt (HATO) based explosives, a comparative study of the bullet impact vulnerable characteristics of HATO-based explosives and HMX-based explosives were carried out by using the 12.7 mm bullet impact test method of the 700-group of the Military Mixed Explosives Formulation and Performance Test Methods. The results of the study indicate that due to the friction and impact sensitivities of the HATO and the HMX explosives are similar, and the bullet impact response degree and the safety performance of the HATO-based explosives and HMX-based explosives is equivalent too.

Coupling fire and energy in the Anthropocene: Deploying scale to analyze social vulnerability to forced electricity outages in California

Extreme events such as wildfires and winter storms result in disruptions to grid-based electricity delivery. Electricity supply disruptions are both reactive, whereby specific events cause damage to physical infrastructure, or anticipatory where electricity suppliers—namely electric utility companies—preemptively de-energize sections of an electrical grid or distribution network based on elevated potential of extreme conditions that may cause wildfire ignition. De-energization has been promoted as a strategy to mitigate risk of wildfire ignition and spread when active fires may encounter distribution/transmission lines. Provision of basic energy services such as electricity are necessary for maintaining a range of essential functions such as communication, which become critical during extreme events. In recent years, Public Safety Power Shutoffs (PSPS) have increasingly been deployed by utility companies in Western U.S states as wildfire risk increases due to combined impacts of anthropogenic climate change, fuel accumulation, and expansion of development in fire prone lands. While the PSPS policy was designed to reduce liability of utilities in igniting fires, there is a dearth of research critically analyzing how the policy affects social vulnerability for populations subjected to periods of de-energization during high-risk fire conditions. This article aims to deepen current understandings of the way scale can be deployed to illustrate the highly spatial nature of relationships coupling electricity supply outages with demographic data to advance limited knowledge on social vulnerability characteristics for specific communities subjected to PSPS. The research engages scale to compare social vulnerability to outages experienced both in Butte County, located in northern California and the state as a whole.

A vulnerability severity prediction method based on bimodal data and multi-task learning

Facing the increasing number of software vulnerabilities, the automatic analysis of vulnerabilities has become an important task in the field of software security. However, the existing severity prediction methods are mainly based on vulnerability descriptions and ignore the relevant features of vulnerability code, which only includes unimodal information and result in low prediction accuracy. This paper proposes a vulnerability severity prediction method based on bimodal data and multi-task learning. First the bimodal data, which consists of the description and source code of each vulnerability, is preprocessed. Next the GraphCodeBert is used for the word embedding module to extract different vulnerability features from the bimodal data. Then the Bi-GRU with attention mechanism is adopted for further feature extraction of vulnerability severity. Considering the strong correlation between the two tasks of vulnerability severity prediction and exploitability prediction, this paper proposes a multi-task learning approach, which allows the model to learn the connection and shared information between different tasks through a hard parameter sharing strategy, so as to achieve more accurate and reliable prediction of vulnerability severity. Experimental results show that the severity prediction method proposed in this paper outperforms state-of-the-art methods, and can achieve an average F1 score of 93.83 % on the public vulnerability dataset.

Hybrid semantics-based vulnerability detection incorporating a Temporal Convolutional Network and Self-attention Mechanism

Context:Desirable characteristics in vulnerability-detection (VD) systems (VDSs) include both good detection capability (high accuracy, low false positive rate, low false negative rate, etc.) and low time overheads. The widely used VDSs based on models such as Recurrent Neural Networks (RNNs) have some problems, such as low time efficiency, failing to learn the vulnerability features better, and insufficient amounts of vulnerability features. Therefore, it is very important to construct an automatic detection model with high detection accuracy. Objective:This paper reports on training based on the source code to analyze and learn from the code’s patterns and structures by deep-learning techniques to generate an efficient VD model that does not require manual feature design. Method:We propose a software VD model based on multi-feature fusion and deep neural networks called AIdetectorX-SP. It first uses a Temporal Convolutional Network (TCN) and adds a Self-attention Mechanism (SaM) to the TCN to build a model for extracting vulnerability logic features, then transforms the source code into an image input to a Convolutional Neural Network (CNN) to extract structural and semantic information. Finally, we use feature-fusion technology to design and implement an improved deep-learning-based VDS, called AIdetectorX Sequence with Picturization (AIdetectorX-SP). Results:We report on experiments conducted using publicly-available and widely-used datasets to evaluate the effectiveness of AIdetectorX-SP, with results indicating that AIdetectorX-SP is an effective VDS; that the combination of TCN and SaM can effectively extract vulnerability logic features; and that the pictorial code can extract code structure features, which can further improve the VD capability. Conclusion:In this paper, we propose a novel detection model for software vulnerability based on TCNs, SaM, and software picturization. The proposed model solves some shortcomings and limitations of existing VDSs, and obtains a high software-VD accuracy with a high degree of stability.

Cross-sectional angle prediction of lipid-rich and calcified tissue on computed tomography angiography images

PurposeThe assessment of vulnerable plaque characteristics and distribution is important to stratify cardiovascular risk in a patient. Computed tomography angiography (CTA) offers a promising alternative to invasive imaging but is limited by the fact that the range of Hounsfield units (HU) in lipid-rich areas overlaps with the HU range in fibrotic tissue and that the HU range of calcified plaques overlaps with the contrast within the contrast-filled lumen. This paper is to investigate whether lipid-rich and calcified plaques can be detected more accurately on cross-sectional CTA images using deep learning methodology.MethodsTwo deep learning (DL) approaches are proposed, a 2.5D Dense U-Net and 2.5D Mask-RCNN, which separately perform the cross-sectional plaque detection in the Cartesian and polar domain. The spread-out view is used to evaluate and show the prediction result of the plaque regions. The accuracy and F1-score are calculated on a lesion level for the DL and conventional plaque detection methods.ResultsFor the lipid-rich plaques, the median and mean values of the F1-score calculated by the two proposed DL methods on 91 lesions were approximately 6 and 3 times higher than those of the conventional method. For the calcified plaques, the F1-score of the proposed methods was comparable to those of the conventional method. The median F1-score of the Dense U-Net-based method was 3% higher than that of the conventional method.ConclusionThe two methods proposed in this paper contribute to finer cross-sectional predictions of lipid-rich and calcified plaques compared to studies focusing only on longitudinal prediction. The angular prediction performance of the proposed methods outperforms the convincing conventional method for lipid-rich plaque and is comparable for calcified plaque.

Disparities in Social Vulnerability and Premature Mortality among Decedents with Hepatitis B, United States, 2010-2019.

Current US hepatitis B mortality rates remain three times higher than the national target. Mortality reduction will depend on addressing hepatitis B disparities influenced by social determinants of health. This study aims to describe characteristics of hepatitis B-listed decedents, which included US birthplace status and county social vulnerability attributes and quantify premature mortality. We conducted a cross-sectional analysis of 17,483 hepatitis B-listed decedents using the 2010-2019 US Multiple-Cause-of-Death data merged with the county-level Social Vulnerability Index (SVI). Outcomes included the distribution of decedents according to US birthplace status and residence in higher versus lower death burden counties by sociodemographic characteristics, years of potential life lost (YPLL), and SVI quartiles. Most hepatitis B-listed decedents were US-born, male, and born during 1945-1965. Median YPLL was 17.2; 90.0% died prematurely. US-born decedents were more frequently White, non-college graduates, unmarried, and had resided in a county with < 500,000 people; non-US-born decedents were more frequently Asian/Pacific Islander, college graduates, married, and had resided in a county with ≥ 1 million people. Higher death burden (≥ 20) counties were principally located in coastal states. US-born decedents more frequently resided in counties in the highest SVI quartile for "Household Characteristics" and "Uninsured," whereas non-US-born decedents more frequently resided in counties in the highest SVI quartile for "Racial/Ethnic Minority Status" and "Housing Type/Transportation." This analysis found substantial premature hepatitis B mortality and residence in counties ranked high in social vulnerability. Successful interventions should be tailored to disproportionately affected populations and the social vulnerability features of their geographic areas.