Chemically-modified hafnium diboride for hypersonic applications: synthesis and characterisation

Hypersonic flight at a speed greater than Mach 5 (1715 ms-1) requires materials that can withstand temperatures up to 3000°C, high heat flux, rapid heating and disassociated reactive oxygen in the extreme environment of space and during re-entry. A number of advanced ceramic materials have melting points over 3000°C, of which the refractory metal carbides and borides are of main interest due to their excellent thermal conductivity from room temperature to over 2500°C, good chemical stability and ablation resistance at high temperatures. These materials are classified as ultra-high-temperature ceramics (UHTCs). Among the family of UHTCs, ZrB2 and HfB2 are reported as the most promising candidates to be used as thermal protection systems (TPS) for the nose tip and sharp leading edges. However, the issue of using monolithic ZrB2 and HfB2 is the phase transformation of ZrO2 and HfO2 oxide by-products at elevated temperature, leading to a volume change that results in cracking of the formed oxide scale. Hence, it is necessary to use dopants to stabilize the oxidation products of ZrB2 and HfB2 in-situ and to minimise the transformation induced cracking and thus improving the oxidation resistance. This research is focused on introducing dopants, such as Y and Ta into HfB2 and to understand its effect on the oxidation behaviour of HfB2 based UHT ceramics. The primary objectives were to: (a) Synthesize sub-micron pure and doped HfB2 powders; (b) Sinter the HfB2 based ceramics to achieve relative density >95% (i.e. with close porosity); (c) Assess the effect of dopants on the oxidation resistance of HfB2 ceramics at high temperatures. Sub-micron pure HfB2 powder of ~200 nm was synthesized by a modified sol-gel approach combined with subsequent carbothermal reduction process using hafnium tetrachloride, boric acid, and phenolic resin as the starting materials. HfC and residual carbon were found to be the main impurity phase, owing to the lack of removal of carbon-containing species in the argon atmosphere during the heat treatment. Therefore, a precipitation approach was developed to transfer hafnium tetrachloride into hafnium hydroxide during the mixing stage to get rid of the Cl- and carbon-containing functional groups. Based on the detailed study of the formation mechanism of HfB2, it was found that the particle size of the HfB2 powders was decided by the particle size of the starting Hf source. Although the powders were slightly coarser (~400-800 nm) from the precipitation approach, importantly phase-pure HfB2 was formed at the same furnace heating conditions (1600°C/2 hrs). The precipitation method was also used to prepare doped HfB2 powders as the homogeneity of the dopants (TaB2, Y2O3) could be improved by controlling the pH values at ~8.5 to achieve the simultaneous precipitation of the dopants and HfB2 precursors. As a result, (Hf,Ta)B2 solid solution was prepared successfully at the temperature of 1600°C. Spark plasma sintering (SPS) was used to densify the pure and doped HfB2 powders. The optimized density achieved was around 97% at 2150°C without the use of any sintering aids and the addition of TaB2 slightly improved the sinterability of the HfB2 based powders due to the formation of the (Hf,Ta)B2 solid solution. The sintered density of commercial micron HfB2 powders (Treibacher) was only 94% in the same condition, and the resultant grain size (5-10 µm) is also significantly larger than that from synthesized HfB2-based ceramics (2-6 µm). The oxide impurities, such as HfO2 and B2O3, on the surface of the fine HfB2 based powders were attributed as the main reason for inhibiting further densification. The oxidation behaviours of the HfB2 based ceramics were investigated via both static oven oxidation and oxyacetylene torch testing. In low and intermediate temperature regime (1600°C), it was found the oxidation product was mainly tetragonal HfO2, which was stabilized by the Ta-dopants at temperatures well below the HfO2 phase transformation temperature. Therefore, the cracking and volume change due to phase transformation can be avoided and in return, oxidation resistance was improved at high temperature, which should be beneficial for the application of these materials in hypersonic aviation

Genetic and immunological insights into COVID-19 with acute myocardial infarction: Integrated analysis of mendelian randomization, transcriptomics, and clinical samples

Background: Globally, most deaths result from cardiovascular diseases, particularly ischemic heart disease. COVID-19 affects the heart, worsening existing heart conditions and causing myocardial injury. The mechanistic link between COVID-19 and acute myocardial infarction (AMI) is still being investigated to elucidate the underlying molecular perspectives. Methods: Genetic risk assessment was conducted using two-sample Mendelian randomization (TSMR) to determine the causality between COVID-19 and AMI. Weighted gene co-expression network analysis (WGCNA) and machine learning were used to discover and validate shared hub genes for the two diseases using bulk RNA sequencing (RNA-seq) datasets. Additionally, gene set enrichment analysis (GSEA) and single-cell RNA-seq (scRNA-seq) analyses were performed to characterize immune cell infiltration, communication, and immune correlation of the hub genes. To validate the findings, the expression patterns of hub genes were confirmed in clinical blood samples collected from COVID-19 patients with AMI. Results: TSMR did not find evidence supporting a causal association between COVID-19 or severe COVID-19 and AMI. In the bulk RNA-seq discovery cohorts for both COVID-19 and AMI, WGCNA’s intersection analysis and machine learning identified TLR4 and ABCA1 as significant hub genes, demonstrating high diagnostic and predictive value in the RNA-seq validation cohort. Single-gene GSEA and single-sample GSEA (ssGSEA) revealed immune and inflammatory roles for TLR4 and ABCA1, linked to various immune cell infiltrations. Furthermore, scRNA-seq analysis unveiled significant immune dysregulation in COVID-19 patients, characterized by altered immune cell proportions, phenotypic shifts, enhanced cell-cell communication, and elevated TLR4 and ABCA1 in CD16 monocytes. Lastly, the increased expression of TLR4, but not ABCA1, was validated in clinical blood samples from COVID-19 patients with AMI. Conclusion: No genetic causal link between COVID-19 and AMI and dysregulated TLR4 and ABCA1 may be responsible for the development of immune and inflammatory responses in COVID-19 patients with AMI

Genetic and immunological insights into COVID-19 with acute myocardial infarction: integrated analysis of mendelian randomization, transcriptomics, and clinical samples

BackgroundGlobally, most deaths result from cardiovascular diseases, particularly ischemic heart disease. COVID-19 affects the heart, worsening existing heart conditions and causing myocardial injury. The mechanistic link between COVID-19 and acute myocardial infarction (AMI) is still being investigated to elucidate the underlying molecular perspectives.MethodsGenetic risk assessment was conducted using two-sample Mendelian randomization (TSMR) to determine the causality between COVID-19 and AMI. Weighted gene co-expression network analysis (WGCNA) and machine learning were used to discover and validate shared hub genes for the two diseases using bulk RNA sequencing (RNA-seq) datasets. Additionally, gene set enrichment analysis (GSEA) and single-cell RNA-seq (scRNA-seq) analyses were performed to characterize immune cell infiltration, communication, and immune correlation of the hub genes. To validate the findings, the expression patterns of hub genes were confirmed in clinical blood samples collected from COVID-19 patients with AMI.ResultsTSMR did not find evidence supporting a causal association between COVID-19 or severe COVID-19 and AMI. In the bulk RNA-seq discovery cohorts for both COVID-19 and AMI, WGCNA’s intersection analysis and machine learning identified TLR4 and ABCA1 as significant hub genes, demonstrating high diagnostic and predictive value in the RNA-seq validation cohort. Single-gene GSEA and single-sample GSEA (ssGSEA) revealed immune and inflammatory roles for TLR4 and ABCA1, linked to various immune cell infiltrations. Furthermore, scRNA-seq analysis unveiled significant immune dysregulation in COVID-19 patients, characterized by altered immune cell proportions, phenotypic shifts, enhanced cell-cell communication, and elevated TLR4 and ABCA1 in CD16 monocytes. Lastly, the increased expression of TLR4, but not ABCA1, was validated in clinical blood samples from COVID-19 patients with AMI.ConclusionNo genetic causal link between COVID-19 and AMI and dysregulated TLR4 and ABCA1 may be responsible for the development of immune and inflammatory responses in COVID-19 patients with AMI

Learning to Abstain From Uninformative Data

Learning and decision-making in domains with naturally high noise-to-signal ratio, such as Finance or Healthcare, is often challenging, while the stakes are very high. In this paper, we study the problem of learning and acting under a general noisy generative process. In this problem, the data distribution has a significant proportion of uninformative samples with high noise in the label, while part of the data contains useful information represented by low label noise. This dichotomy is present during both training and inference, which requires the proper handling of uninformative data during both training and testing. We propose a novel approach to learning under these conditions via a loss inspired by the selective learning theory. By minimizing this loss, the model is guaranteed to make a near-optimal decision by distinguishing informative data from uninformative data and making predictions. We build upon the strength of our theoretical guarantees by describing an iterative algorithm, which jointly optimizes both a predictor and a selector, and evaluates its empirical performance in a variety of settings

PARP9 affects myocardial function through TGF-β/Smad axis and pirfenidone

Cardiac arrhythmias are often linked to the overactivity of cardiac fibroblasts (CFs). Investigating the impact of poly (ADP-ribose) polymerase 9 (PARP9) on Angiotensin II (Ang II)-induced fibroblast activation and the therapeutic effects of pirfenidone (PFD) offers valuable insights into cardiac arrhythmias. This study utilized weighted gene co-expression network analysis (WGCNA), differential gene expression (DEG) analysis, protein-protein interaction (PPI), and receiver operating characteristic (ROC) analysis on the GSE42955 dataset to identify the hub gene with significant diagnostic value. The ImmuCellAI tool revealed an association between PARP9 and immune cell infiltration. Our in vitro assessments focused on the influence of PFD on myofibroblast differentiation, TGF-β expression, and Ang II-induced proliferation and migration in CFs. Additionally, we explored the impact on fibrosis markers and the TGF-β/Smad signaling pathway in the context of PARP9 overexpression. Analysis of the GSE42955 dataset revealed PARP9 as a central gene with high clinical diagnostic value, linked to seven types of immune cells. The in vitro studies demonstrated that PFD significantly mitigates Ang II-induced CF proliferation, migration, and fibrosis. It also reduces Ang II-induced PARP9 expression and decreases fibrosis markers, including TGF-β, collagen I, collagen III, and α-SMA. Notably, PARP9 overexpression can partially counteract PFD's inhibitory effects on CFs and modify the expression of fibronectin, CTGF, α-SMA, collagen I, collagen III, MMP2, MMP9, TGF-β, and p-Smad2/3 in the TGF-β/Smad signaling pathway. In summary, our findings suggestes that PFD effectively counteracts the adverse effects of Ang II-induced CF proliferation and fibrosis, and modulates the TGF-β/Smad signaling pathway and PARP9 expression. This identifies a potential therapeutic approach for managing myocardial fibrosis

GL-Segnet: Global-Local representation learning net for medical image segmentation

Medical image segmentation has long been a compelling and fundamental problem in the realm of neuroscience. This is an extremely challenging task due to the intensely interfering irrelevant background information to segment the target. State-of-the-art methods fail to consider simultaneously addressing both long-range and short-range dependencies, and commonly emphasize the semantic information characterization capability while ignoring the geometric detail information implied in the shallow feature maps resulting in the dropping of crucial features. To tackle the above problem, we propose a Global-Local representation learning net for medical image segmentation, namely GL-Segnet. In the Feature encoder, we utilize the Multi-Scale Convolution (MSC) and Multi-Scale Pooling (MSP) modules to encode the global semantic representation information at the shallow level of the network, and multi-scale feature fusion operations are applied to enrich local geometric detail information in a cross-level manner. Beyond that, we adopt a global semantic feature extraction module to perform filtering of irrelevant background information. In Attention-enhancing Decoder, we use the Attention-based feature decoding module to refine the multi-scale fused feature information, which provides effective cues for attention decoding. We exploit the structural similarity between images and the edge gradient information to propose a hybrid loss to improve the segmentation accuracy of the model. Extensive experiments on medical image segmentation from Glas, ISIC, Brain Tumors and SIIM-ACR demonstrated that our GL-Segnet is superior to existing state-of-art methods in subjective visual performance and objective evaluation

The distribution shifts of Pinus armandii and its response to temperature and precipitation in China

Background The changing climate, particularly in regard to temperature and precipitation, is already affecting tree species’ distributions. Pinus armandii, which dominates on the Yungui Plateau and in the Qinba Mountains in China, is of economic, cultural and ecological value. We wish to test the correlations between the distribution shift of P. armandii and changing climate, and figure out how it tracks future climate change. Methods We sampled the surface soil at sites throughout the distribution of P. armandii to compare the relative abundance of pollen to the current percent cover of plant species. This was used to determine possible changes in the distribution P. armandii. Given the hilly terrain, elevation was considered together with temperature and precipitation as variables correlated with distribution shifts of P. armandii. Results We show that P. armandii is undergoing change in its geographic range, including retraction, a shift to more northern areas and from the upper high part of the mountains to a lower-altitude part in hilly areas. Temperature was the strongest correlate of this distribution shift. Elevation and precipitation were also both significantly correlated with distribution change of P. armandii, but to a lesser degree than temperature. Conclusion The geographic range of P. armandii has been gradually decreasing under the influence of climate change. This provides evidence of the effect of climate change on trees at the species level and suggests that at least some species will have a limited ability to track the changing climate