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The privacy protection of the internet of vehicles resource transaction details based on blockchain

The rapid development of Internet of Things technology has promoted the popularization of Internet of Vehicles, and its safety and reliability have become the focus of intelligent transportation system research. Vehicle-road collaboration relies on the collaborative computing and storage resources of the vehicle on-board unit (OBU), which are usually limited. When the vehicle in the edge area needs to do computing tasks such as intelligent driving, but its own computing resources are insufficient. Therefore, it needs other computing resources from idle vehicles and road side unit (RSU). This resource sharing can get additional computing resources to complete the task, and can be more convenient to complete the computing task quickly. Most current studies consider this type of resource sharing as a vehicle-to-vehicle (V2V) network transaction, aiming to stimulate the enthusiasm of vehicle sharing and optimize the utilization of computing resources in edge areas. However, the traditional blockchain transaction mode exposes serious privacy disclosure risks in vehicle networking resource transactions, including the openness and transparency of user identity, transaction details, and transaction addresses, which poses great challenges to data security. Therefore, this study innovatively proposed a blockchain-based privacy protection scheme for vehicle networking resource transaction details. By introducing committed value protection, zero-knowledge proof technology and constructing temporary transaction addressed mechanism. The scheme realized the comprehensive privacy protection of transaction funds, transaction details and transaction addresses, which could effectively avoid the disclosure of users’ sensitive information. Compared with the existing methods, the proposed scheme not only greatly enhanced the privacy protection capability, but also ensured the efficiency and security of transaction verification through zero-knowledge proof, avoiding the direct exposure of private keys. Meanwhile, the experimental verification demonstrates that the scheme not only enhances the level of privacy protection but also does not augment the supplementary processing burden. Furthermore, it is evident that the scheme meets the rigorous requirements for real-time resource transactions in the Internet of Vehicles.

Correction: Exploring the acceptability of a community-enhanced intervention to improve decision support partnership between patients with chronic kidney disease and their family caregivers

Trends in childhood cancer: Incidence and survival analysis over 45 years of SEER data

Background The SEER Registry contains U.S. cancer statistics. To assess trends in incidence and survival and the impact of demographic factors among pediatric patients with cancer, we assessed nearly 5 decades (1975–2019) of data. Methods All patients below the age of 20 with histology-confirmed malignancy were studied. Kaplan-Meier survival curves were generated to evaluate survival trends across treatment periods and ICCC classes. JoinPoint analysis was conducted to identify changes in incidence and survival. Results The incidence of childhood cancer increased from 14.23 cases per 100,000 children in 1975–1979 to 18.89 in 2010–2019, with an average annual percent change of 0.73. This rise was more pronounced in several cancers, including leukemias, lymphomas, brain tumors, hepatic tumors, and gonadal germ cell tumors. Age-adjusted cancer mortality decreased from 4.9 to 2.3 per 100,000. Cancer-related mortality was consistently higher in boys than in girls, and in Black children than in White children. Survival significantly improved, with 5- and 10-year survival rates rising from 63.1% to 85.2% and from 58.8% to 82.7%, respectively. Leukemias showed a substantial increase in 5-year survival from 48.2% ± 1.7% to 85.1% ± 0.4% in 2010–2019. Lymphomas also showed significant improvement, with survival increasing from 72.9% ± 1.7% to 94.2% ± 0.3%. Despite these improvements, the survival of CNS tumors, bone tumors, and sarcomas remained suboptimal, with 5-year survival estimates of approximately 60%. Our joinpoint analysis confirmed our findings but revealed an interesting increase in the incidence of lymphomas limited to the years between 2005 and 2014. Conclusion This research elucidates advancements in survival among pediatric patients with cancer. The results offer critical perspectives on pediatric oncology, highlighting the imperative for ongoing innovation in therapeutics. Although the increase in incidence may partially stem from enhanced diagnostic capabilities and more comprehensive registration processes, the underlying causes remain unclear.

Bias in adjudication: Investigating the impact of artificial intelligence, media, financial and legal institutions in pursuit of social justice

The latest global progress report highlights numerous challenges in achieving justice goals, with bias in artificial intelligence (AI) emerging as a significant yet underexplored issue. This paper investigates the role of AI in addressing bias within the judicial system to promote equitable social justice. Analyzing weekly data from January 1, 2019, to December 31, 2023, through wavelet quantile correlation, this study examines the short, medium, and long-term impacts of integrating AI, media, international legal influence (ILI), and international financial institutions (IFI) as crucial factors in achieving Sustainable Development Goal 16 (SDG-16), which focuses on justice. The findings indicate that AI, media, ILI, and IFI can help reduce bias in the medium and long term, although their effects appear mixed and less significant in the short term. Our research proposes a comprehensive policy framework that addresses the complexities of implementing these technologies in the judicial system. We conclude that successfully integrating AI requires a supportive global policy environment that embraces technological innovation, financial backing, and robust regulation to prevent potential disruptions that could reinforce inequalities, perpetuate structural injustices, and exacerbate human rights issues, ultimately leading to more biased outcomes in social justice.

Occult metastases and survival of lung cancer by clinical diagnosis and CT screening: A simulation study

Objectives It is significant to know how much early detection and screening could reduce the proportion of occult metastases and benefit NSCLC patients. Methods We used previously designed and validated mathematical models to obtain the characteristics of LC in the population including undetectable metastases at the time of diagnosis. The survival was simulated using the survival functions from Surveillance, Epidemiology and End Results (SEER) data stratified by stage. Results Based on the simulations, 35.3% of patients diagnosed with stage N0M0 and 56.9% of those diagnosed with stage N1M0 had nodal or distant metastases that were not discovered at the time of diagnosis. Among clinically detected Stage I lung cancers with tumor diameter 1–2 cm, 78% were true stage N0M0 (no occult metastases) while it was only 37% for patients with tumor diameters of 2–3 cm. This size threshold can be translated into a 0.75-year the “window of opportunity” for the curable disease. In a comparative analysis of two simulated groups of individuals: (1) clinically diagnosed (2) diagnosed by screening with a varying screening frequency (quarterly, biannual, annual and biennial), it was estimated that, once the screening intervals become shorter, substantially more cancers are found, but at an expense of a higher radiation exposure. The simulation projected that the mortality reduction in screened patients depending on the frequency, ranged from 15.04% to 18.82%. Conclusions The probability of occult metastases significantly increases when the primary tumor exceeds 2 cm in diameter. Effective screening measures that detect smaller tumors will considerably benefit asymptomatic LC patients.

Risk factors for overtaking, rear-end, and door crashes involving bicycles in the United Kingdom: Revisited and reanalysed

Background and objective Relevant research has provided valuable insights into risk factors for bicycle crashes at intersections. However, few studies have focused explicitly on three common types of bicycle crashes on road segments: overtaking, rear-end, and door crashes. This study aims to identify risk factors for overtaking, rear-end, and door crashes that occur on road segments. Material and methods We analysed British STATS19 accident records from 1991 to 2020. Using multivariate logistic regression models, we estimated adjusted odds ratios (AORs) with 95% confidence intervals (CIs) for multiple risk factors. The analysis included 127,637 bicycle crashes, categorised into 18,350 overtaking, 44,962 rear-end, 6,363 door, and 57,962 other crashes. Results Significant risk factors for overtaking crashes included heavy goods vehicles (HGVs) as crash partners (AOR = 1.30, 95% CI 1.27–1.33), and elderly crash partners (AOR = 2.01, 95% CI = 1.94–2.09), and decreased risk in rural area with speed limits of 20–30 miles per hour (AOR = 0.45, 95% CI = 0.43–0.47). For rear-end crashes, noteworthy risk factors included unlit darkness (AOR = 1.49, 95% CI = 1.40–1.57) and midnight hours (AOR = 1.28, 95% CI = 1.21–1.40). Factors associated with door crashes included urban areas (AOR = 16.2, 95% CI = 13.5–19.4) and taxi or private hire cars (AOR = 1.61, 95% CI = 1.57–1.69). Our joint-effect analysis revealed additional interesting results; for example, there were elevated risks for overtaking crashes in rural areas with elderly drivers as crash partners (AOR = 2.93, 95% CI = 2.79–3.08) and with HGVs as crash partners (AOR = 2.62, 95% CI = 2.46–2.78). Conclusions The aforementioned risk factors remained largely unchanged since 2011, when we conducted our previous study. However, the present study concluded that the detrimental effects of certain variables became more pronounced in certain situations. For example, cyclists in rural settings exhibited an elevated risk of overtaking crashes involving HGVs as crash partners.

Correction: Evaluation of circulating microRNA profiles in Brazilian women with polycystic ovary syndrome: A preliminary study

Neural network-based aeroelastic system identification for predicting flutter of high flexibility wings

Study on construction mechanic characteristics and construction optimization of super-large cross-section tunnel portal under shallow buried depth and asymmetrical loading: A case study in Southwest China

The excavation of the super-large cross-section tunnel portal section is prone to causing serious engineering distresses. The key factors to ensure the safe construction of portal section are to clarify the construction mechanic characteristics and select a reasonable construction scheme. In this paper, a bidirectional six-lane expressway tunnel in Southwest China was selected as an engineering case. Three excavation schemes, namely, the three-bench seven-step excavation method (TEM), the central diaphragm method (CDM), and the double side drift method (DSDM), were compared and analyzed. Findings revealed that due to the effect of the asymmetrically loaded and super-large cross-section, the surrounding rock deformation and supporting structure stress at the deep buried side were greater than those at the shallow buried side. The CDM and DSDM could reduce the tunnel span and provide temporary support in time, which could effectively control the surrounding rock deformation and improve the structural stress and the slope stability. According to the topographic condition, the excavation sequence of the DSDM was optimized. Excavating the shallow buried side drift first could alleviate the surrounding rock deformation and improve the slope stability in the early stage of construction. Finally, the optimal excavation scheme was successfully implemented.

The effect of steering axis inclination on the dynamic stability of vehicles with an articulated steering

Association between serum vitamin D and severe headache or migraine: A population-based analysis

Background Vitamin D is thought to play a role in the development of migraine, but the nature of the relationship is still not fully understood. Although some studies have shown an association between vitamin D deficiency and migraine, other studies have had inconsistent or inconclusive results. Therefore, further research is needed to better understand the relationship between vitamin D and migraine headaches. Methods We selected 9142 participants from the 2001–2004 National Health and Nutrition Examination Survey (NHANES). In our study, the term "serum vitamin D" refers to the concentration of 25OHD2 + 25OHD3 (nmol/L) in the blood. Migraine was assessed based on self-reports in the miscellaneous pain section of the NHANES questionnaire. Associations between vitamin D and the risk of migraine were examined using multiple logistic regression, smoothed curve fitting, and stratified analyses. Results In our study, 20.53% of the participants suffered from migraine. The prevalence of migraine was higher in those with lower serum vitamin D levels. Participants in the highest quartile of serum vitamin D levels were found to have a 16% lower prevalence than those in the lowest quartile in the fully adjusted model (OR = 0.84, 95% CI 0.71–0.99). This result was supported by stratified analysis and smoothed curve fitting. Conclusion Our study showed a significant negative correlation between serum vitamin D levels and the prevalence of migraine in American adults.

Gastrointestinal absorption and its regulation of hawthorn leaves flavonoids

Association between exclusive or dual use of combustible cigarettes and heated tobacco products and depressive symptoms

Purpose Despite the advent of heated tobacco products (HTPs), their relationship to mental health remains unclear. This study aimed to determine associations between the use of combustible cigarettes (CCs) and HTPs with depressive symptoms. Methods This descriptive-analytical cross-sectional study was conducted in March 2023. Using the 8th Korea National Health and Nutrition Examination Survey, 5,349 adults aged 19 years or older were classified into four groups: non-users, CC-only users, HTP-only users, and dual users. Relationships between exclusive or dual use of CCs and HTPs and depressive symptoms were analyzed using item scores and total scores of the Patient Health Questionnaire-9 (PHQ-9). To examine associations between exclusive or dual use of CCs and HTPs and depressive symptoms, a multinomial regression analysis was performed using the PHQ-9 total score. Results HTP-only users had the highest proportion of those with anhedonia and depressed mood. CC-only users had the highest proportion of individuals with trouble sleeping, while dual users had a higher proportion of those with fatigue and appetite problems. After adjusting for general characteristics, compared to non-users, CC-only users were more likely to have mild and moderate to severe depressive symptoms. HTP-only users and dual users were also more likely to have moderate to severe depressive symptoms. Conclusions All smokers have a higher risk of depression than non-smokers. Health care providers should closely monitor depressive symptoms, especially in HTP users and dual users of tobacco products.

Shared rare genetic variants in multiplex autism families suggest a social memory gene under selection

Investigating the contributors to hit-and-run crashes using gradient boosting decision trees

A classification prediction model is established based on a nonlinear method—Gradient Boosting Decision Tree (GBDT) to investigate the factors contributing to a perpetrator’s escape behavior in hit-and-run crashes. Given the U.S. Crash Report Sampling System (CRSS) dataset, the model is trained and compared with the state-of-art methods (Classification and Regression Tree, Random Forest, and Logistic Regression). The results show that the GBDT outperforms other methods, achieving the lowest negative log-likelihood (0.282), misclassification rate (0.096), and the highest AUC (0.803). GBDT also demonstrates superior computational efficiency, with a LIFT value of 4.087, making it a more accurate and efficient model for predicting hit-and-run crashes compared to CART, Random Forest, and Logistic Regression. The results obtained from the GBDT show that the relative importance of crash type and relation to trafficway rank 4th and 5th, respectively. Neither is mentioned in previous studies, indicating that GBDT has the ability to mine hidden information. In addition, the interaction between influencing variables can also be obtained to investigate the joint effect of various variables. The results of this study have practical applications in hit-and-run incident prevention, accident safety analysis, and other engineering applications.

Subfamily evolution analysis using nuclear and chloroplast data from the same reads

Using DNA metabarcoding and direct behavioural observations to identify the diet of proboscis monkeys (Nasalis larvatus) in the Kinabatangan Floodplain, Sabah

Characterizing the feeding ecology of threatened species is essential to establish appropriate conservation strategies. We focused our study on the proboscis monkey (Nasalis larvatus), an endangered primate species which is endemic to the island of Borneo. Our survey was conducted in the Lower Kinabatangan Wildlife Sanctuary (LKWS), a riverine protected area that is surrounded by oil palm plantations. We aimed to determine the diet of multiple proboscis monkey groups by using two methods. First, we conducted boat-based direct observations (scan and ad libitum sampling) and identified 67 plant species consumed by the monkeys at their sleeping sites in early mornings and late afternoons. Secondly, we used the DNA metabarcoding approach, based on next-generation sequencing (NGS, MiSeq Illumina) of faecal samples (n = 155), using the short chloroplast sequence, the trnL (UAA) P6 loop. In addition, we built a DNA reference database with the local plants available in the LKWS. When combining feeding data from both methods, we reported a diverse dietary ecology in proboscis monkeys, with at least 89 consumed plant taxa, belonging to 76 genera and 45 families. Moreover, we were able to add 22 new genera as part of the diet of this endangered colobine primate in the LKWS. The two methods provided congruent and complementary results, both having their advantages and limitations. This study contributed to enhance the knowledge on the feeding ecology of proboscis monkeys, highlighting the significance of several plant species that should further be considered in habitat restoration plans or corridor establishment.

Common biomarkers of idiopathic pulmonary fibrosis and systemic sclerosis based on WGCNA and machine learning

AbstractInterstitial lung disease (ILD) is known to be a major complication of systemic sclerosis (SSc) and a leading cause of death in SSc patients. As the most common type of ILD, the pathogenesis of idiopathic pulmonary fibrosis (IPF) has not been fully elucidated. In this study, weighted correlation network analysis (WGCNA), protein‒protein interaction, Kaplan–Meier curve, univariate Cox analysis and machine learning methods were used on datasets from the Gene Expression Omnibus database. CCL2 was identified as a common characteristic gene of IPF and SSc. The genes associated with CCL2 expression in both diseases were enriched mainly in chemokine-related pathways and lipid metabolism-related pathways according to Gene Set Enrichment Analysis. Single-cell RNA sequencing (sc-RNAseq) revealed a significant difference in CCL2 expression in alveolar epithelial type 1/2 cells, mast cells, ciliated cells, club cells, fibroblasts, M1/M2 macrophages, monocytes and plasma cells between IPF patients and healthy donors. Statistical analyses revealed that CCL2 was negatively correlated with lung function in IPF patients and decreased after mycophenolate mofetil (MMF) treatment in SSc patients. Finally, we identified CCL2 as a common biomarker from IPF and SSc, revealing the common mechanism of these two diseases and providing clues for the study of the treatment and mechanism of these two diseases.

Automated CAD system for early detection and classification of pancreatic cancer using deep learning model

Accurate diagnosis of pancreatic cancer using CT scan images is critical for early detection and treatment, potentially saving numerous lives globally. Manual identification of pancreatic tumors by radiologists is challenging and time-consuming due to the complex nature of CT scan images and variations in tumor shape, size, and location of the pancreatic tumor also make it challenging to detect and classify different types of tumors. Thus, to address this challenge we proposed a four-stage framework of computer-aided diagnosis systems. In the preprocessing stage, the input image resizes into 227 × 227 dimensions then converts the RGB image into a grayscale image, and enhances the image by removing noise without blurring edges by applying anisotropic diffusion filtering. In the segmentation stage, the preprocessed grayscale image a binary image is created based on a threshold, highlighting the edges by Sobel filtering, and watershed segmentation to segment the tumor region and we also implement the U-Net method for segmentation. Then refine the geometric structure of the image using morphological operation and extracting the texture features from the image using a gray-level co-occurrence matrix computed by analyzing the spatial relationship of pixel intensities in the refined image, counting the occurrences of pixel pairs with specific intensity values and spatial relationships. The detection stage analyzes the tumor region’s extracted features characteristics by labeling the connected components and selecting the region with the highest density to locate the tumor area, achieving a good accuracy of 99.64%. In the classification stage, the system classifies the detected tumor into the normal, pancreatic tumor, then into benign, pre-malignant, or malignant using a proposed reduced 11-layer AlexNet model. The classification stage attained an accuracy level of 98.72%, an AUC of 0.9979, and an overall system average processing time of 1.51 seconds, demonstrating the capability of the system to effectively and efficiently identify and classify pancreatic cancers.

Canagliflozin alleviates acetaminophen-induced renal and hepatic injury in mice by modulating the p-GSK3β/Fyn-kinase/Nrf-2 and p-AMPK-α/STAT-3/SOCS-3 pathways

AbstractDespite the fact that canagliflozin (Cana), a sodium-glucose cotransporter 2 inhibitor, is an anti-diabetic medication with additional effects on the kidney, there is limited experimental data to deliberate its hepato-reno-protective potentiality. Acetaminophen (APAP) overdose remains one of the prominent contributors to hepato-renal damage. Aim: Our study assessed the novel effect of Cana against APAP-induced toxicities. Main methods: mice were randomized into five groups: negative control, Cana25, APAP, Cana10 + APAP, and Cana25 + APAP. Cana was given for 5 days; a single dose of APAP was injected on the 6th day, followed by the scarification of animals 24 h later. Key findings: Pre-treatment with Cana ameliorated hepatic and renal functions, whereas, on the molecular levels, Cana promoted hepatic/renal P-AMP-activated protein kinase-α/ protein kinase B (p-Akt)/Glycogen synthase kinase (p-GSK3β) protein expression. Alternatively, Cana dampened the expression of STAT-3 and Fyn-kinase genes with a subsequent increase in the contents of suppressor of cytokine signaling (SOCS)-3 and also boosted the contents of the nuclear factor erythroid related factor 2 (Nrf-2)/heme oxygenase (HO)-1/ NADPH quinone oxidoreductase (NQO)-1 axis. The crosstalk between these paths ameliorated the APAP-induced hepatorenal structural alterations. Significance: Cana hepatorenal protective impact was provoked partly through modulating p-AMPK-α /SOCS-3/STAT-3 and GSK3β/Fyn-kinase signaling for its anti-inflammatory and antioxidant effects.