The loss of endothelial cells is associated with the accumulation of monocytes/macrophages underneath the surface of the arteries, where cells are prone to mechanical stimulation, such as shear ...stress. However, the impact of mechanical stimuli on monocytic cells remains unclear. To assess whether mechanical stress affects monocytic cell function, we examined the expression of inflammatory molecules and surface proteins, whose levels changed following shear stress in human THP-1 cells. Shear stress increased the inflammatory chemokine CCL2, which enhanced the migration of monocytic cells and tumor necrosis factor (TNF)-α and interleukin (IL)− 1β at transcriptional and protein levels. We identified that the surface levels of heat shock protein 70 (HSP70), HSP90, and HSP105 increased using mass spectrometry-based proteomics, which was confirmed by western blot analysis, flow cytometry, and immunofluorescence. Treatment with HSP70/HSP105 and HSP90 inhibitors suppressed the expression and secretion of CCL2 and monocytic cell migration, suggesting an association between HSPs and inflammatory responses. We also demonstrated the coexistence and colocalization of increased HSP90 immunoreactivity and CD68 positive cells in atherosclerotic plaques of ApoE deficient mice fed a high-fat diet and human femoral artery endarterectomy specimens. These results suggest that monocytes/macrophages affected by shear stress polarize to a pro-inflammatory phenotype and increase surface protein levels involved in inflammatory responses. The regulation of the abovementioned HSPs upregulated on the monocytes/macrophages surface may serve as a novel therapeutic target for inflammation due to shear stress.
Display omitted
•Shear stress upregulates the monocyte/macrophage expression and secretion of pro-inflammatory molecules.•We have identified the membrane proteins associated with macrophage function.•We have demonstrated macrophage expression of HSP90 in human and mouse atherosclerotic plaque specimens under conditions prone to shear stress.
•This paper is a study on posterior inference for generative models.•The whole inference process consists of sampling/simulation/inference parts.•The proposed method improves the sampling part of the ...inference process.•The paper replaces the sampling algorithm with a neural sampler.•The neural sampler outperforms the baseline samplers by its design.
Bayesian inference without the likelihood evaluation, or likelihood-free inference, has been a key research topic in simulation studies for gaining quantitatively validated simulation models on real-world datasets. As the likelihood evaluation is inaccessible, previous papers train the amortized neural network to estimate the ground-truth posterior for the simulation of interest. Training the network and accumulating the dataset alternatively in a sequential manner could save the total simulation budget by orders of magnitude. In the data accumulation phase, the new simulation inputs are chosen within a portion of the total simulation budget to accumulate upon the collected dataset so far. This newly accumulated data degenerates because the set of simulation inputs is hardly mixed, and this degenerated data collection process ruins the posterior inference. This paper introduces a new sampling approach, called Neural Proposal (NP), of the simulation input that resolves the biased data collection as it guarantees the i.i.d. sampling. The experiments show the improved performance of our sampler, especially for the simulations with multi-modal posteriors.
Due to enhancements in Internet of Things (IoT) technology, users can now control numerous IoT devices that are integrated for a system (e.g., smart building management system, smart factory, etc.) ...through mobile user equipment (UE). As the number of controllable IoT devices increases, the amount of data that needs to be processed has increased along with the energy consumption. Since many IoT devices and most UEs are battery operated, minimizing the energy consumption is very important. One solution is to have a multiaccess edge computing (MEC) system conduct the computation instead of the IoT devices and UEs to help save their energy. In this article, an energy-optimized offloading approach that uses MEC computing support to process cooperative tasks is investigated. An optimization problem model is developed to minimize the energy consumption of IoT devices and UEs that have service delay limits. The numerical results demonstrate that the proposed IoT and UE popularity-based energy optimization (IPEO) scheme provides a better performance compared to the conventional MEC offloading methods.
Hepatocellular carcinoma (HCC) is one of the most aggressive malignancies. Recently, the overexpression of programmed cell death 1 (PD-1) and PD-1 ligand 1 (PD-L1) has been shown to correlate with ...poor prognosis in many cancers. However, the expression of PD-L1 or PD-1 ligand 2 (PD-L2) and clinical outcomes have not been fully investigated in HCC.
Formalin-fixed paraffin-embedded samples were obtained from 85 patients with HCC who underwent surgery. The expression of PD-Ls (PD-L1, PD-L2) was evaluated by immunohistochemical analysis.
The proportion of high expression groups of PD-L1 and PD-L2 was 27.1% and 23.5%, respectively. Univariate analysis revealed that tumor size (p < 0.001), histological differentiation (p=0.010), PD-L1 expression (p < 0.001), and PD-L2 expression (p=0.039) were significant prognostic factors of overall survival in patients with HCC. Multivariate analysis revealed that overall tumor size (hazard ratio HR, 4.131; 95% confidence interval CI, 2.233 to 7.643; p < 0.001 and HR, 3.455; 95% CI, 1.967 to 6.067; p < 0.001) and PD-L1 expression (HR, 5.172; 95% CI, 2.661 to 10.054; p < 0.001 and HR, 3.730; 95% CI, 1.453 to 9.574; p=0.006) were independent prognostic values for overall and disease-free survival. Patients with high expression of PD-Ls had a significantly poorer survival than those with low expression (p < 0.001, p=0.034).
The overexpression of PD-Ls in HCC patients is correlated with survival and tumor recurrence. Further evaluation of PD-1 and PD-Ls as therapeutic targets and predictive biomarkers for HCC is warranted.
Heterozygous mutations in the FOXG1 gene manifest as FOXG1 syndrome, a severe neurodevelopmental disorder characterized by structural brain anomalies, including agenesis of the corpus callosum, ...hippocampal reduction, and myelination delays. Despite the well-defined genetic basis of FOXG1 syndrome, therapeutic interventions targeting the underlying cause of the disorder are nonexistent. In this study, we explore the therapeutic potential of adeno-associated virus 9 (AAV9)-mediated delivery of the FOXG1 gene. Remarkably, intracerebroventricular injection of AAV9-FOXG1 to Foxg1 heterozygous mouse model at the postnatal stage rescues a wide range of brain pathologies. This includes the amelioration of corpus callosum deficiencies, the restoration of dentate gyrus morphology in the hippocampus, the normalization of oligodendrocyte lineage cell numbers, and the rectification of myelination anomalies. Our findings highlight the efficacy of AAV9-based gene therapy as a viable treatment strategy for FOXG1 syndrome and potentially other neurodevelopmental disorders with similar brain malformations, asserting its therapeutic relevance in postnatal stages.
Display omitted
FOXG1 syndrome, a neurodevelopmental disorder, results from reduced FOXG1 protein levels due to heterozygous mutations in FOXG1. Lee and colleagues show postnatal injection of AAV9-FOXG1 to Foxg1 heterozygous mice rescues their various structural and cellular anomalies, demonstrating the efficacy of AAV9-based gene therapy as a viable treatment strategy for FOXG1 syndrome.
Transmembrane p24 trafficking protein 3 (TMED3) is a metastatic suppressor in colon cancer and hepatocellular carcinoma. However, its function in the progression of clear cell renal cell carcinoma ...(ccRCC) is unknown. Here, we report that TMED3 could be a new prognostic marker for ccRCC. Patient data were extracted from cohorts in the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Differential expression of TMED3 was observed between the low stage (Stage I and II) and high stage (Stage III and IV) patients in the TCGA and ICGC cohorts and between the low grade (Grade I and II) and high grade (Grade III and IV) patients in the TCGA cohort. Further, we evaluated
expression as a prognostic gene using Kaplan-Meier survival analysis, multivariate analysis, the time-dependent area under the curve (AUC) of Uno's C-index, and the AUC of the receiver operating characteristics at 5 years. The Kaplan-Meier analysis revealed that
overexpression was associated with poor prognosis for ccRCC patients. Analysis of the C-indices and area under the receiver operating characteristic curve further supported this. Multivariate analysis confirmed the prognostic significance of
expression levels (
= 0.005 and 0.006 for TCGA and ICGC, respectively). Taken together, these findings demonstrate that TMED3 is a potential prognostic factor for ccRCC.
From the viewpoint of urban administration, simulation is regarded as a policy tool that provides administrators with information about the current urban situation and enables them to verify the ...effectiveness of urban policies. This study proposes a traffic simulation model for a real city named Sejong in South Korea. Our proposed model employs agent-based simulation with the city-level real data, which mainly focuses on describing the movement behavior of individuals using urban traffics in the real city. By aggregating the agents' decisions and interactions during the movement, the proposed model can discover a demand for the city's transportation system. To do this, this study validated the proposed model so that the modeled traffic system was similar to the real one, and then we conducted a case study to compare and analyze the effects of traffic dispersion led by the upcoming bridge construction in the real city. The case study showed that the proposed model can provide policy evaluation on the optimal location of the bridge construction considering the city traffic flow. Furthermore, the case study presented that the agent-based modeling enables micro-level analysis on the city traffic flow to understand on the policy implications.
Abstract
Background
Antibiotics administered to farm animals have led to increasing prevalence of resistance genes in different microbiomes and environments. While antibiotic treatments help cure ...infectious diseases in farm animals, the possibility of spreading antibiotic resistance genes into the environment and human microbiomes raises significant concerns. Through long-term evolution, antibiotic resistance genes have mutated, thereby complicating the resistance problems.
Results
In this study, we performed deep sequencing of the gut microbiomes of 36 swine and 41 cattle in Korean farms, and metagenomic analysis to understand the diversity and prevalence of antibiotic resistance genes. We found that aminoglycoside, β-lactam, lincosamide, streptogramin, and tetracycline were the prevalent resistance determinants in both swine and cattle. Tetracycline resistance was abundant and prevalent in cattle and swine. Specifically, tetQ, tetW, tetO, tet32, and tet44 were the 5 most abundant and prevalent tetracycline resistance genes. Their prevalence was almost 100% in swine and cattle. While tetQ was similarly abundant in both swine and cattle, tetW was more abundant in swine than in cattle. Aminoglycoside was the second highest abundant resistance determinant in swine, but not in cattle. In particular, ANT(6) and APH(3′′) were the dominant resistance gene families in swine. β-lactam was also an abundant resistance determinant in both swine and cattle. Cfx was the major contributing gene family conferring resistance against β-lactams.
Conclusions
Antibiotic resistome was more pervasive in swine than in cattle. Specifically, prevalent antibiotic resistance genes (prevalence >50%) were found more in swine than in cattle. Genomic investigation of specific resistance genes from the gut microbiomes of swine and cattle in this study should provide opportunities to better understand the exchange of antibiotic resistance genes in farm animals.
This work proposes a novel precision motion control framework of robotized industrial hydraulic excavators via data-driven model inversion. Rather than employing a single neural network to ...approximate the whole excavator dynamics, including input delays and dead-zones, we construct a physics-inspired data-driven model with a modular structure. The data-driven model is then inverted in a modular fashion which benefits the training speed. The data-driven model and its inversion are trained offline in a supervised manner using the real operational data since online learning methods can damage the machine and surroundings. The entire motion control framework consists of the data-driven model inversion that compensates for the excavator dynamics and the proportional control that determines the input of the model inversion to enhance the robustness. The framework is experimentally validated with a commercial 38-ton class hydraulic excavator for digging and grading tasks, achieving a precise control performance (i.e., root-mean-square of the path following error under <inline-formula><tex-math notation="LaTeX">2 \;\rm cm</tex-math></inline-formula>) even under severe soil interactions.
PDF
