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Correction: Knowledge, attitude, and perceptions about polycystic ovarian syndrome, and its determinants among Pakistani undergraduate students

Generative adversarial local density-based unsupervised anomaly detection

Anomaly detection is crucial in areas such as financial fraud identification, cybersecurity defense, and health monitoring, as it directly affects the accuracy and security of decision-making. Existing generative adversarial nets (GANs)-based anomaly detection methods overlook the importance of local density, limiting their effectiveness in detecting anomaly objects in complex data distributions. To address this challenge, we introduce a generative adversarial local density-based anomaly detection (GALD) method, which combines the data distribution modeling capabilities of GANs with local synthetic density analysis. This approach not only considers different data distributions but also incorporates neighborhood relationships, enhancing anomaly detection accuracy. First, by utilizing the adversarial process of GANs, including the loss function and the rarity of anomaly objects, we constrain the generator to primarily fit the probability distribution of normal objects during the unsupervised training process; Subsequently, a synthetic dataset is sampled from the generator, and the local synthetic density, which is defined by measuring the inverse of the sum of distances between a data point and all objects in its synthetic neighborhood, is calculated; Finally, the objects that show substantial density deviations from the synthetic data are classified as anomaly objects. Extensive experiments on seven real-world datasets from various domains, including medical diagnostics, industrial monitoring, and material analysis, were conducted using seven state-of-the-art anomaly detection methods as benchmarks. The GALD method achieved an average AUC of 0.874 and an accuracy of 94.34%, outperforming the second-best method by 7.2% and 6%, respectively.

Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction

Diabetes, a chronic condition affecting millions worldwide, necessitates early intervention to prevent severe complications. While accurately predicting diabetes onset or progression remains challenging due to complex and imbalanced datasets, recent advancements in machine learning offer potential solutions. Traditional prediction models, often limited by default parameters, have been superseded by more sophisticated approaches. Leveraging Bayesian optimization to fine-tune XGBoost, researchers can harness the power of complex data analysis to improve predictive accuracy. By identifying key factors influencing diabetes risk, personalized prevention strategies can be developed, ultimately enhancing patient outcomes. Successful implementation requires meticulous data management, stringent ethical considerations, and seamless integration into healthcare systems. This study focused on optimizing the hyperparameters of an XGBoost ensemble machine learning model using Bayesian optimization. Compared to grid search XGBoost (accuracy: 97.24%, F1-score: 95.72%, MCC: 81.02%), the XGBoost with Bayesian optimization achieved slightly improved performance (accuracy: 97.26%, F1-score: 95.72%, MCC:81.18%). Although the improvements observed in this study are modest, the optimized XGBoost model with Bayesian optimization represents a promising step towards revolutionizing diabetes prevention and treatment. This approach holds significant potential to improve outcomes for individuals at risk of developing diabetes.

Trump tariffs and the U.S. defense industry

In March 2018, U.S. President Trump announced that the U.S. would start imposing tariffs on steel and aluminum imports from most exporting countries around the world. This study explores the impact of introducing these tariffs on the equity return of U.S. defense companies. As the defense industry stands among the largest metal consumers in the U.S., it is expected that these import restrictions have deteriorated the business performance of the U.S. defense industry. For this study, a novel trade uncertainty indicator has been constructed that is based on the key events related to the invocation of Section 232 of the Trade Expansion Act. This section empowers the President to impose trade restrictions when the quantity of imports threatens to impair national security. My empirical analysis reveals that investors perceived the introduction of the steel and aluminum tariffs as detrimental to U.S. defense companies. The negative abnormal stock returns in the days around several key tariff-related events evidence this. Already in the period before the Department of Commerce released the findings of its investigation, investors were speculating on the possible introduction of trade barriers. However, the height of the imposed tariff exceeded their expectations since the negative sentiment was further reinforced after the official announcement of the tariff by President Trump.

Differential GTP-dependent in-vitro polymerization of recombinant Physcomitrella FtsZ proteins

Abstract Bacterial cell division and plant chloroplast division require selfassembling Filamentous temperature-sensitive Z (FtsZ) proteins. FtsZ proteins are GTPases sharing structural and biochemical similarities with eukaryotic tubulin. In the moss Physcomitrella, the morphology of the FtsZ polymer networks varies between the different FtsZ isoforms. The underlying mechanism and foundation of the distinct networks is unknown. Here, we investigated the interaction of Physcomitrella FtsZ2-1 with FtsZ1 isoforms via co-immunoprecipitation and mass spectrometry, and found protein-protein interaction in vivo. We tagged FtsZ1-2 and FtsZ2-1 with different fluorophores and expressed both in E. coli , which led to the formation of defined structures within the cells and to an influence on bacterial cell division and morphology. Furthermore, we have optimized the purification protocols for FtsZ1-2 and FtsZ2-1 expressed in E. coli and characterized their GTPase activity and polymerization in vitro. Both FtsZ isoforms showed GTPase activity. Stoichiometric mixing of both proteins led to a significantly increased GTPase activity, indicating a synergistic interaction between them. In light scattering assays, we observed GTP-dependent assembly of FtsZ1-2 and of FtsZ2-1 in a protein concentration dependent manner. Stoichiometric mixing of both proteins resulted in significantly faster polymerization, again indicating a synergistic interaction between them. Under the same conditions used for GTPase and light scattering assays both FtsZ isoforms formed filaments in a GTP-dependent manner as visualized by transmission electron microscopy (TEM). Taken together, our results reveal that Physcomitrella FtsZ1-2 and FtsZ2-1 are functionally different, can synergistically interact in vivo and in vitro, and differ in their properties from FtsZ proteins from bacteria, archaea and vascular plants.

Islet NO-Synthases, extracellular NO and glucose-stimulated insulin secretion: Possible impact of neuronal NO-Synthase on the pentose phosphate pathway

The impact of islet neuronal nitric oxide synthase (nNOS) on glucose-stimulated insulin secretion (GSIS) is less understood. We investigated this issue by performing simultaneous measurements of the activity of nNOS versus inducible NOS (iNOS) in GSIS using isolated murine islets. Additionally, the significance of extracellular NO on GSIS was studied. Islets incubated at basal glucose showed modest nNOS but no iNOS activity. Glucose-induced concentration-response studies revealed an increase in both NOS activities in relation to secreted insulin. Culturing at high glucose increased both nNOS and iNOS activities inducing a marked decrease in GSIS in a following short-term incubation at high glucose. Culturing at half-maximal glucose showed strong iNOS expression revealed by fluorescence microscopy also in human islets. Experiments with nNOS-inhibitors revealed that GSIS was inversely related to nNOS activity, the effect of iNOS activity being negligible. The increased GSIS after blockade of nNOS was reversed by the intracellular NO-donor hydroxylamine. The enhancing effect on GSIS by nNOS inhibition was independent of membrane depolarization and most likely exerted in the pentose phosphate pathway (PPP). GSIS was markedly reduced, 50%, by glucose-6-phosphate dehydrogenase (G-6-PD) inhibition both in the absence and presence of nNOS inhibition. NO gas stimulated GSIS at low and inhibited at high NO concentrations. The stimulatory action was dependent on membrane thiol groups. In comparison, carbon monoxide (CO) exclusively potentiated GSIS. CO rather than NO stimulated islet cyclic GMP during GSIS. It is suggested that increased nNOS activity restrains GSIS, and that the alternative pathway along the PPP initially might involve as much as 50% of total GSIS. In the PPP, the acute insulin response is downregulated by a negative feedback effect executed by a marked upregulation of nNOS activity elicited from secreted insulin exciting insulin receptors at exocytotic sites of an nNOS-associated population of secretory granules.

Utility of complexity analysis in electroencephalography and electromyography for automated classification of sleep-wake states in mice

Structural design and safety performance of a novel high-strength steel lightweight guardrail

Highway guardrails are critical safety infrastructure along roadways, designed to redirect vehicles back into their lanes and facilitate a gradual deceleration to a complete stop. Traditional highway steel guardrails exhibit significant limitations, including inadequate energy absorption, susceptibility to corrosion, and an increased risk of vehicles leaving the roadway during severe collisions. Furthermore, the production and transportation of these guardrails contribute to substantial carbon emissions and environmental pollution. This study presents an optimization of the cross-sectional shape of the conventional corrugated beam guardrail, proposing a lightweight structure that incorporates high yield strength steel plate HR700F to enhance energy absorption capacity. The safety performance of the proposed guardrail is rigorously assessed through finite element numerical simulations and full-scale collision tests with real vehicles. Key performance indicators—such as the maximum dynamic lateral deflection of the guardrail, occupant impact velocity, and acceleration—are utilized to evaluate the energy absorption and protective efficacy of the structure. Results indicate that the optimized guardrail not only meets SB-level safety standards but also demonstrates superior anti-collision performance and effective energy absorption and buffering characteristics. The proposed design achieves a reduction in beam plate thickness by 1.3 mm, resulting in a lightweight structure with a weight reduction of up to 44%, thereby supporting the advancement of low-carbon, environmentally sustainable transportation solutions.

Impact of frequent ARID1A mutations on protein stability provides insights into cancer pathogenesis

Low regulatory T-cells frequency is associated with graft rejection after small bowel transplantation: Clinical and experimental evidence

Background Intestinal transplantation (ITx) represents the only curative option for patients with irreversible intestinal failure. Nevertheless, its rejection rate surpasses that of other solid organ transplants due to the heightened immunological load of the gut. Regulatory T-cells (Tregs) are key players in the induction and maintenance of peripheral tolerance, suggesting their potential involvement in modulating host vs. graft responses after ITx. Thus, we investigated the association of Tregs with allograft outcomes in pediatric patients and in an experimental model of small bowel transplantation. Methods Treg frequency in human samples was analyzed by Flow cytometry (CD4+CD25highCD127-, blood samples) and immunohistochemistry (FoxP3, graft samples). Experimental allogenic-heterotopic small bowel transplantation was performed in rats and animals divided into 3 groups: non-immunosuppressant treatment, rapamycin (2 mg/kg), and tacrolimus (0.6 mg/kg) treatment. Acute cellular rejection (ACR) was diagnosed based on clinical and histological findings, graft gene expression of pro- and anti-inflammatory mediators assessed by RT-qPCR, serum IL-6 and IL-10 levels by Luminex, and Treg frequency analyzed by flow cytometry (CD4+CD25highFoxP3+). Results Blood samples from patients undergoing ACR exhibited a significant reduction in the Treg number compared to those with normo-functional grafts. Similarly, a diminished number of FoxP3+ cells was observed in mucosa samples with ACR. In the experimental model, rapamycin-treated animals displayed clinical and histological findings resembling those not receiving immunosuppression treatment. Notably, ACR correlated with a high CD8/CD4 ratio, loss of T-cell chimerism, mRNA upregulation of pro-inflammatory genes and diminished graft Treg frequency. In contrast, tacrolimus treatment prevented ACR and facilitate blood and graft Treg expansion. Remarkably, recipients who achieved Treg expansion within the graft remained free of ACR even after discontinuation of the immunosuppressant treatment and this phenomenon was associated with increased levels of serum IL-10. Conclusion Our clinical and experimental findings underscore the association between Treg frequency and graft rejection after ITx, advocating for strategies that promote their expansion within the gut mucosa to enhance long-term outcomes.

Influence of the COVID-19 pandemic on the prevalence of depression in U.S. adults: evidence from NHANES

Exploring real estate blockchain adoption: An empirical study based on an integrated task-technology fit and technology acceptance model

Although many organizations have adopted blockchain technology (BCT) for efficiency and automation and users’ adoption of BCT is becoming crucial for the companies, few studies have focused on the factors affecting the adoption of BCT in real estate sector. This study aims to assess the factors affecting the adoption of BCT in real estate by the lens of an extended Technology Acceptance Model (TAM) and Task-technology fit (TTF). This current study uses quantitative survey approach. Data were collected from 311 real sector buyers and sellers in China. Partial least square structural equation modeling (PLS-SEM) was used for data analysis. The study’s findings indicate that attitude, perceived usefulness (PU) and data privacy and security (DPS) exerts highest influence in the proposed theoretical model. In addition, the findings also confirmed the significant impact of perceived ease of use (PEOU) on attitude, and TTF impact on PU and PEOU in adoption of BCT. Furthermore, the moderating impact of perceived compatibility (PC) on the relationship between TTF and PEOU was also confirmed. The study has useful practical implications for the real estate buyers and sellers that adoption of BCT improves efficiency, reduce transaction cost incur due to intermediaries, and enhance security system. Furthermore, the study suggests that automation achieved through BCT will facilitate customized agreements and improve process of title transfer.

Decreased PD-L1 contributes to preeclampsia by suppressing GM-CSF via the JAK2/STAT5 signal pathway

AI-assisted radiologists vs. standard double reading for rib fracture detection on CT images: A real-world clinical study

To evaluate the diagnostic accuracy of artificial intelligence (AI) assisted radiologists and standard double-reading in real-world clinical settings for rib fractures (RFs) detection on CT images. This study included 243 consecutive chest trauma patients (mean age, 58.1 years; female, 166) with rib CT scans. All CT scans were interpreted by two radiologists. The CT images were re-evaluated by primary readers with AI assistance in a blinded manner. Reference standards were established by two musculoskeletal radiologists. The re-evaluation results were then compared with those from the initial double-reading. The primary analysis focused on demonstrate superiority of AI-assisted sensitivity and the noninferiority of specificity at patient level, compared to standard double-reading. Secondary endpoints were at the rib and lesion levels. Stand-alone AI performance was also assessed. The influence of patient characteristics, report time, and RF features on the performance of AI and radiologists was investigated. At patient level, AI-assisted radiologists significantly improved sensitivity by 25.0% (95% CI: 10.5, 39.5; P < 0.001 for superiority), compared to double-reading, from 69.2% to 94.2%. And, the specificity of AI-assisted diagnosis (100%) was noninferior to double-reading (98.2%) with a difference of 1.8% (95% CI: -3.8, 7.4; P = 0.999 for noninferiority). The diagnostic accuracy of both radiologists and AI was influenced by patient gender, rib number, fracture location, and fracture type. Radiologist performance was affected by report time, whereas AI’s diagnostic accuracy was influenced by patient age and the side of the rib involved. AI-assisted additional-reader workflow might be a feasible strategy to instead of traditional double-reading, potentially offering higher sensitivity and specificity compared to standard double-reading in real-word clinical practice.

Multiomic machine learning on lactylation for molecular typing and prognosis of lung adenocarcinoma

Efficacy of different routes of triamcinolone acetonide administration on macular edema: A systematic review and network meta-analysis

There is different administration routes of triamcinolone acetonide (TA) administration for macular edema, but the efficacy ranking remains unclear. The purpose of this study is to assess the efficacy of different administration routes of TA employed in macular edema. PubMed, Medline, Embase, and Cochrane Central Register of Controlled Trials were systematically searched for published articles comparing macular edema in patients with triamcinolone acetonide in different administration. The sparse network was evaluated using a random-effects model and consistency model within the Bayesian framework, utilizing the multinma package in R. The evidence was assessed based on the Grading of Recommendations. Assessment, Development, and Evaluation (GRADE) criteria. A total of 1138 citations were identified by our search, of which 20 RCTs enrolled 892 eyes. The network showed that intravitreal triamcinolone acetonide (IVTA) was associated with a statistically significant better best corrected visual acuity (BCVA) at the 12th week compared to placebo (MD: − 0.15, 95% CI: − 0.30 to − 0.01, P < 0.05), which was moderate-quality evidence. IVTA and suprachoroidal triamcinolone acetonide (SCTA) were both associated with a statistically significant reduction in central macular thickness (CMT) at the 12th week, which was moderate evidence. The probabilities of rankings and SUCRA demonstrated that sub-Tenon’s infusion of triamcinolone acetonide (STiTA) might be the worst. SCTA and IVTA were proven to be the best administration routes for improving BCVA and reducing CMT. In addition, STiTA was less advisable than other administration routes of triamcinolone acetonide according to the rankings and SUCRA.

Primary tumor resection might improve outcomes in metastatic thoracic esophageal cancer

Examining hurricane–related social media topics longitudinally and at scale: A transformer-based approach

Instead of turning to emergency phone systems, social media platforms, such as Twitter, have emerged as alternative and sometimes preferred venues for members of the public in the US to communicate during hurricanes and other natural disasters. However, relevant posts are likely to be missed by responders given the volume of content on platforms. Previous work successfully identified relevant posts through machine-learned methods, but depended on human annotators. Our study indicates that a GPU-accelerated version of BERTopic, a transformer-based topic model, can be used without human training to successfully discern topics during multiple hurricanes. We use 1.7 million tweets from four US hurricanes over seven years and categorize identified topics as temporal constructs. Some of the more prominent topics related to disaster relief, user concerns, and weather conditions. Disaster managers can use our model, data, and constructs to be aware of the types of themes social media users are producing and consuming during hurricanes.

Connecting electronic health records to a biomedical knowledge graph to link clinical phenotypes and molecular endotypes in atopic dermatitis

A spatial interpolation method based on 3D-CNN for soil petroleum hydrocarbon pollution

Petroleum hydrocarbon pollution causes significant damage to soil, so accurate prediction and early intervention are crucial for sustainable soil management. However, traditional soil analysis methods often rely on statistical methods, which means they always rely on specific assumptions and are sensitive to outliers. Existing machine learning based methods convert features containing spatial information into one-dimensional vectors, resulting in the loss of some spatial features of the data. This study explores the application of Three-Dimensional Convolutional Neural Networks (3DCNN) in spatial interpolation to evaluate soil pollution. By introducing Channel Attention Mechanism (CAM), the model assigns different weights to auxiliary variables, improving the prediction accuracy of soil hydrocarbon content. We collected soil pollution data and validated the spatial distribution map generated using this method based on the drilling dataset. The results indicate that compared with traditional Kriging3D methods (R2 = 0.318) and other machine learning methods such as support vector regression (R2 = 0.582), the proposed 3DCNN based method can achieve better accuracy (R2 = 0.954). This approach provides a sustainable tool for soil pollution management, supports decision-makers in developing effective remediation strategies, and promotes the sustainable development of spatial interpolation techniques in environmental science.