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The phase fraction plays a critical role in determining the solidification characteristics of metallic alloys. In this study, we propose a novel method (fs = (t − tl)/(ts − tl)) for estimating the phase fraction based on the solidification time in cooling curves. This method was validated through an experimental analysis of Al-18 wt%Cu and Fe42Ni42B16 alloys, where the phase fractions derived from cooling curves were compared with quantitative microstructure evaluations using computer-aided image analysis and the box-counting method. Then, a comparison between the analysis using the present novel method and Newtonian thermal analysis demonstrates good agreement between the results. The present method is easier to operate, since it does not need derivative and integral operations as in Newtonian thermal analysis. In addition, based on the characteristics of the cooling curve, we also found two other relationships—V/Rc = D/ΔTc and RΔt = constant, where V is the solidification rate, Rc is the recalescence rate, D is the diameter of the focal area of the pyrometer, ΔTc is the recalescence height, R is the cooling rate, and Δt is the solidification plateau time. These findings establish an operational framework for quantifying phase fractions and solidification rates in rapid solidification.

Redactable Blockchain aims to ensure the immutability of the data of most applications and provide authorized mutability for some specific applications, such as for removing illegal content from blockchains. However, the existing Redactable Blockchains lack redacting efficiency and protection of the identity information of voters participating in the redacting consensus. To fill this gap, this paper presents an anonymous and efficient redactable blockchain scheme based on Proof-of-Work (PoW) in the permissionless setting, called “AeRChain”. Specifically, the paper first presents an improved Back’s Linkable Spontaneous Anonymous Group (bLSAG) signatures scheme and uses the improved scheme to hide the identity of blockchain voters. Then, in order to accelerate the achievement of redacting consensus, it introduces a moderate puzzle with variable target values for selecting voters and a voting weight function for assigning different weights to puzzles with different target values. The experimental results show that the present scheme can achieve efficient anonymous redacting consensus with low overhead and reduce communication traffic.

This paper considers a resource allocation problem involving servers and mobile users (MUs) operating in a serverless edge computing (SEC)-enabled Internet of Things (IoT) network. Each MU has a fixed budget, and each server is powered by the grid and has energy harvesting (EH) capability. Our objective is to maximize the revenue of the operator that operates the said servers and the number of resources purchased by the MUs. We propose a Stackelberg game approach, where servers and MUs act as leaders and followers, respectively. We prove the existence of a Stackelberg game equilibrium and develop an iterative algorithm to determine the final game equilibrium price. Simulation results show that the proposed scheme is efficient in terms of the SEC’s profit and MU’s demand. Moreover, both MUs and SECs gain benefits from renewable energy.

The deformation behavior of the mush zone for superalloy during investment casting directly affects the dimensional control of casting has puzzled many engineers and scientists for years. Numerical simulations are not directly useful to predict the most suitable pattern allowances. A new data-driven approach to be effectively used for pattern allowance and casting process parameters prediction is proposed. The constitutive relationships and deformation parameter from high-temperature mechanical tests on superalloy K4169 is reported. The inputs are alloy temperature, shell temperature, and pattern allowance with the outputs of diameter and ovality of the ring-to-ring casting, respectively. It turns out that the shell temperature is the most momentous factor that governs the dimensional variability in ovality. An RBF-based approximation model is established and the optimized parameters are the alloy temperature 1500.5°C, shell temperature1052.5°C, and the pattern allowance 1.7258%. The optimized results agree well with the observed in practical casting and the ring-to-ring casting tolerance has been optimized as required within CT6 grade. The proposed method is believed to benefit to provide theoretical guidance for casting practice. The data-driven approach used in this research can be easily applied to different materials and different kinds of casting that are subject to dimensional control upon solidification.

Although centrality is widely used to differentiate the importance of nodes for social-aware routing in mobile opportunistic networks (MONs), it is destination-agnostic since such metrics are usually measured without destination information. To this end, we propose a destination-aware social routing scheme for MONs, namely DAS, which utilizes the destination-aware betweenness centrality (DBC) to choose the right nodes as relays given the specific destination node. During the process of message dissemination, the number of replicas for a message is calculated by the source and each relay independently in respond to the network condition in a dynamic manner. Therefore, DAS disseminates only a few message copies to ensure data delivery as well as reducing routing cost. We conduct extensive simulations using real trace data sets to show improved performance with low overhead in comparison with existing social-aware routing approaches in various scenarios.

Background Despite advancements in the successful use of immunotherapy in treating a variety of solid tumors, applications in treating brain tumors have lagged considerably. This is due, at least in part, to the lack of well-characterized antigens expressed within brain tumors that can mediate tumor rejection; the low mutational burden of these tumors that limits the abundance of targetable neoantigens; and the immunologically “cold” tumor microenvironment that hampers the generation of sustained and productive immunologic responses. The field of mRNA-based therapeutics has experienced a boon following the universal approval of COVID-19 mRNA vaccines. mRNA-based immunotherapeutics have also garnered widespread interest for their potential to revolutionize cancer treatment. In this study, we developed a novel and scalable approach for the production of personalized mRNA-based therapeutics that target multiple tumor rejection antigens in a single therapy for the treatment of refractory brain tumors. Methods Tumor-specific neoantigens and aberrantly overexpressed tumor-associated antigens were identified for glioblastoma and medulloblastoma tumors using our cancer immunogenomics pipeline called O pen R eading Frame A ntigen N etwork (O.R.A.N). Personalized tumor antigen-specific mRNA vaccine was developed for each individual tumor model using selective gene capture and enrichment strategy. The immunogenicity and efficacy of the personalized mRNA vaccines was evaluated in combination with anti-PD-1 immune checkpoint blockade therapy or adoptive cellular therapy with ex vivo expanded tumor antigen-specific lymphocytes in highly aggressive murine GBM models. Results Our results demonstrate the effectiveness of the antigen-specific mRNA vaccines in eliciting robust anti-tumor immune responses in GBM hosts. Our findings substantiate an increase in tumor-infiltrating lymphocytes characterized by enhanced effector function, both intratumorally and systemically, after antigen-specific mRNA-directed immunotherapy, resulting in a favorable shift in the tumor microenvironment from immunologically cold to hot. Capacity to generate personalized mRNA vaccines targeting human GBM antigens was also demonstrated. Conclusions We have established a personalized and customizable mRNA-therapeutic approach that effectively targets a plurality of tumor antigens and demonstrated potent anti-tumor response in preclinical brain tumor models. This platform mRNA technology uniquely addresses the challenge of tumor heterogeneity and low antigen burden, two key deficiencies in targeting the classically immunotherapy-resistant CNS malignancies, and possibly other cold tumor types.

Glioblastoma (GBM) generates a varied immune response and understanding the immune microenvironment may lead to novel immunotherapy treatments modalities. The goal of this study was to evaluate the expression of immunologic markers of potential clinical significance in primary versus recurrent GBM and assess the relationship between these markers and molecular characteristics of GBM. Human GBM samples were evaluated and analyzed with immunohistochemistry for multiple immunobiologic markers (CD3, CD8, FoxP3, CD68, CD163, PD1, PDL1, CTLA4, CD70). Immunoreactivity was analyzed using Aperio software. Degree of strong positive immunoreactivity within the tumor was compared to patient and tumor characteristics including age, gender, MGMT promoter methylation status, and ATRX, p53, and IDH1 mutation status. Additionally, the TCGA database was used to perform similar analysis of these factors in GBM using RNA-seq by expectation–maximization. Using odds ratios, IDH1 mutated GBM had statistically significant decreased expression of CD163 and CD70 and a trend for decreased PD1, CTLA4, and Foxp3. ATRX-mutated GBMs exhibited statistically significant increased CD3 immunoreactivity, while those with p53 mutations were found to have significantly increased CTLA4 immunoreactivity. The odds of having strong CD8 and CD68 reactivity was significantly less in MGMT methylated tumors. No significant difference was identified in any immune marker between the primary and recurrent GBM, nor was a significant change in immunoreactivity identified among age intervals. TCGA analysis corroborated findings related to the differential immune profile of IDH1 mutant, p53 mutant, and MGMT unmethylated tumors. Immunobiologic markers have greater association with the molecular characteristics of the tumor than with primary/recurrent status or age.

The durability and protective effect of naturally acquired antibodies against hepatitis E virus (HEV) reinfection and clinical progression remain unclear in humans. In a 103-month longitudinal analysis of 7032 adult placebo recipients (aged 16 to 65 years) from a phase 3 HEV vaccine trial in China, we demonstrated that baseline anti-HEV IgG seropositivity (n = 3194) conferred over 50% higher protection against reinfection compared with seronegative individuals (n = 3838), with this protective effect remaining consistent over 8.5 years. A non-linear dose-response relationship was observed, whereby baseline anti-HEV IgG concentrations ≥0.25 WHO units/mL were associated with at least a 50% reduction in infection risk, with higher baseline antibody levels correlated with a lower risk of infection. Natural immunity provided approximately 70% protection against clinically apparent hepatitis E in the cohort, with 10 symptomatic cases identified over a decade of active surveillance. Six were hospitalized, all of whom were baseline seronegative. These findings establish that natural HEV immunity provides durable, though incomplete, protection. Hepatitis E virus is a leading cause of acute viral hepatitis and antibodies can persist for years post-infection. Here, the authors quantify the protective effects of naturally acquired immunity against subclinical and clinical hepatitis E infection using data from a placebo arm of a vaccine trial in China.

Polymerase I and transcript release factor ( PTRF ) plays a role in the regulation of gene expression and the release of RNA transcripts during transcription, which have been associated with various human diseases. However, the role of PTRF in glioma remains unclear. In this study, RNA sequencing (RNA-seq) data ( n  = 1022 cases) and whole-exome sequencing (WES) data ( n  = 286 cases) were used to characterize the PTRF expression features. Gene ontology (GO) functional enrichment analysis was used to assess the biological implication of changes in PTRF expression. As a result, the expression of PTRF was associated with malignant progression in gliomas. Meanwhile, somatic mutational profiles and copy number variations (CNV) revealed the glioma subtypes classified by PTRF expression showed distinct genomic alteration. Furthermore, GO functional enrichment analysis suggested that PTRF expression was associated with cell migration and angiogenesis, particularly during an immune response. Survival analysis confirmed that a high expression of PTRF is associated with a poor prognosis. In summary, PTRF may be a valuable factor for the diagnosis and treatment target of glioma.

Background The current standard of care treatments for medulloblastoma are insufficient as these do not take tumor heterogeneity into account. Newer, safer, patient-specific treatment approaches are required to treat high-risk medulloblastoma patients who are not cured by the standard therapies. Immunotherapy is a promising treatment modality that could be key to improving survival and avoiding morbidity. For an effective immune response, appropriate tumor antigens must be targeted. While medulloblastoma patients with subgroup-specific genetic substitutions have been previously reported, the immunogenicity of these genetic alterations remains unknown. The aim of this study is to identify potential tumor rejection antigens for the development of antigen-directed cellular therapies for medulloblastoma. Methods We developed a cancer immunogenomics pipeline and performed a comprehensive analysis of medulloblastoma subgroup-specific transcription profiles ( n  = 170, 18 WNT, 46 SHH, 41 Group 3, and 65 Group 4 patient tumors) available through International Cancer Genome Consortium (ICGC) and European Genome-Phenome Archive (EGA). We performed in silico antigen prediction across a broad array of antigen classes including neoantigens, tumor-associated antigens (TAAs), and fusion proteins. Furthermore, we evaluated the antigen processing and presentation pathway in tumor cells and the immune infiltrating cell landscape using the latest computational deconvolution methods. Results Medulloblastoma patients were found to express multiple private and shared immunogenic antigens. The proportion of predicted TAAs was higher than neoantigens and gene fusions for all molecular subgroups, except for sonic hedgehog (SHH), which had a higher neoantigen burden. Importantly, cancer-testis antigens, as well as previously unappreciated neurodevelopmental antigens, were found to be expressed by most patients across all medulloblastoma subgroups. Despite being immunologically cold, medulloblastoma subgroups were found to have distinct immune cell gene signatures. Conclusions Using a custom antigen prediction pipeline, we identified potential tumor rejection antigens with important implications for the development of immunotherapy for medulloblastoma.