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Densified SiC ceramics were prepared by using gel-casting method combined with liquid-phase sintering (LPS). In this study, a low-toxicity gel-casting system based on 2-hydroxyethyl methacrylate (HEMA) and N, N′-methylene acrylamide (MBAM) as monomer and crosslinker respectively, was first applied to SiC fabrication. The effects of the processing parameters on gel-casting were investigated in detail to achieve optimized rheological behavior and green body strength. The best result was obtained in the slurry with solid content of 50 vol%, dispersant of 0.4 wt% and monomer/crosslinker ratio of 20 at pH10. With the modified ternary sintering aids (Al 2 O 3 –Y 2 O 3 –CeO 2) added, the densification can be realized at low temperature. After pressureless sintering at 1850 °C for 2 h, the sample with best mechanical properties was obtained. The highest relative density, hardness, flexural strength and fracture toughness were 96.23%, 24.94 GPa, 423.95 MPa and 4.78 MPa/m1/2, respectively. Present work provides an environmentally friendly and energy-saving process for preparing SiC ceramics with excellent properties. [ABSTRACT FROM AUTHOR]

Iodine is a well-known oxidant that is widely used in organic syntheses. Thiol oxidation by stoichiometric iodine is one of the most commonly employed strategies for the synthesis of valuable disulfides. While recent advancements in catalytic aerobic oxidation conditions have eliminated the need for stoichiometric oxidants, concerns persist regarding the use of toxic or expensive catalysts. In this study, we discovered that iodine can be used as a cheap, low-toxicity catalyst in the aerobic oxidation of thiols. In the catalytic cycle, iodine can be regenerated via HI oxidation by O2 at 70 °C in EtOAc. This protocol harnesses sustainable oxygen as the terminal oxidant, enabling the conversion of primary and secondary thiols with remarkable efficiency. Notably, all 26 tested thiols, encompassing various sensitive functional groups, were successfully converted into their corresponding disulfides with yields ranging from >66% to 98% at a catalyst loading of 5 mol%. [ABSTRACT FROM AUTHOR]

High-brightness laser lighting requires high luminous efficacy and high thermal performance of luminescent materials. In this study, we designed and fabricated single-phase YAG:Ce ceramics with differential grain sizes by combining the coarse commercial phosphor and fine powder of precusor. The large YAG:Ce grains mainly contribute to excellent luminescence properties. The small YAG:Ce grains not only change the direction of light propagation to improve the luminous efficiency but also effectively increase the Ce3+ content to improve the absorption of blue light, which significantly further enhance the luminescence properties compared with the second phase in the YAG:Ce-base composite ceramics. The luminescent ceramic with 40 wt% large grains sintered under 1550 °C has the highest luminous efficacy of 398.5 lm/W. Compared with the Al 2 O 3 –YAG:Ce composite ceramic, the single-phase YAG:Ce ceramic effectively increased the absorption of blue light to enhance the luminescence properties without the adverse effects caused by the transverse scattering of light in the Al 2 O 3 phase. After encapsulating the ceramic onto a radiator, a high luminous flux of 7697.6 lm under the blue-laser excitation of 3.0 A (54.6 W) was obtained. These indicated its broad application prospects in high-power solid-state laser lighting. [ABSTRACT FROM AUTHOR]

This paper presents a self-supervised learning network called SP-Flow to generate keypoints in real-time for SLAM systems. Optical flows are employed to match the keypoints between two successive frames in the training process of SP-Flow. This approach enables the network to use datasets without manual annotations. To show the efficacy of our SP-Flow, we built an SP-Flow SLAM system by replacing ORB with SP-Flow in the ORB-SLAM2 system. The experimental results demonstrate that our SLAM system is able to achieve real-time performance and high accuracy with stereo or RGB-D images as inputs. • A self-supervised framework is used for SP-Flow network training. • The keypoints classified by the optical flow are considered when constructing the loss functions. • A keypoint extractor suitable for SLAM is proposed. • The robustness of SLAM in low-texture environments is improved. [ABSTRACT FROM AUTHOR]

Heterotopic ossification (HO) comprises the abnormal formation of ectopic bone in extraskeletal soft tissue. The factors that initiate HO remain elusive. Herein, we found that calcified apoptotic vesicles (apoVs) led to increased calcification and stiffness of tendon extracellular matrix (ECM), which initiated M2 macrophage polarization and HO progression. Specifically, single‐cell transcriptome analyses of different stages of HO revealed that calcified apoVs were primarily secreted by a PROCR+ fibroblast population. In addition, calcified apoVs enriched calcium by annexin channels, absorbed to collagen I via electrostatic interaction, and aggregated to produce calcifying nodules in the ECM, leading to tendon calcification and stiffening. More importantly, apoV‐releasing inhibition or macrophage deletion both successfully reversed HO development. Thus, we are the first to identify calcified apoVs from PROCR+ fibroblasts as the initiating factor of HO, and might serve as the therapeutic target for inhibiting pathological calcification. [ABSTRACT FROM AUTHOR]

EN1 encodes a homeodomain-containing transcription factor and is a determinant of bone density and fracture. Previous powerful genome-wide association studies (GWASs) have identified multiple single-nucleotide polymorphisms (SNPs) near EN1 at 2q14.2 locus for osteoporosis, but the causal SNPs and functional mechanisms underlying these associations are poorly understood. The target genes regulated by the transcription factor EN1 are also unclear. In this study, we identified rs188303909, a functional CpG-SNP, as a causal SNP for osteoporosis at 2q14.2 through the integration of functional and epigenomic analyses. Functional experiments demonstrated that unmethylated rs188303909 acted as a strong allele-specific distal enhancer to regulate EN1 expression by modifying the binding of transcription factor E2F6, but rs188303909 methylation attenuated the active effect of E2F6 on EN1 expression. Importantly, transcription factor EN1 could differentially bind osteoporosis GWAS lead SNPs rs4869739-T and rs4355801-G to upregulate CCDC170 and COLEC10 expression, thus promoting bone formation. Our study provided a mechanistic insight into expression regulation of the osteoporosis susceptibility gene EN1, which could be a potential therapeutic target for osteoporosis precision medicine. Key messages: CpG-SNP rs188303909 is a causal SNP at the osteoporosis susceptibility locus 2q14.2. Rs188303909 distally regulates EN1 expression by modulating DNA methylation and E2F6 binding. EN1 upregulates CCDC170 and COLEC10 expression through osteoporosis GWAS lead SNPs rs4869739 and rs4355801. [ABSTRACT FROM AUTHOR]

Detection of subsurface hydrodynamic anomalies plays a significant role in groundwater resource management and environmental monitoring. In this paper, based on data from the groundwater level, atmospheric pressure, and precipitation in the Chengdu area of China, a method for detecting outliers considering the factors affecting groundwater levels is proposed. By analyzing the factors affecting groundwater levels in the monitoring site and eliminating them, simplified groundwater data is obtained. Applying sl-Pauta (self-learning-based Pauta), iForest (Isolated Forest), OCSVM (One-Class SVM), and KNN to synthetic data with known outliers, testing and evaluating the effectiveness of 4 technologies. Finally, the four methods are applied to the detection of outliers in simplified groundwater levels. The results show that in the detection of outliers in synthesized data, the OCSVM method has the best detection performance, with a precision rate of 88.89%, a recall rate of 91.43%, an F1 score of 90.14%, and an AUC value of 95.66%. In the detection of outliers in simplified groundwater levels, a qualitative analysis of the displacement data within the field of view indicates that the outlier detection performance of iForest and OCSVM is better than that of KNN. The proposed method for considering the factors affecting groundwater levels can improve the efficiency and accuracy of detecting outliers in groundwater level data. [ABSTRACT FROM AUTHOR]

Detection of subsurface hydrodynamic anomalies plays a significant role in groundwater resource management and environmental monitoring. In this paper, based on data from the groundwater level, atmospheric pressure, and precipitation in the Chengdu area of China, a method for detecting outliers considering the factors affecting groundwater levels is proposed. By analyzing the factors affecting groundwater levels in the monitoring site and eliminating them, simplified groundwater data is obtained. Applying sl-Pauta (self-learning-based Pauta), iForest (Isolated Forest), OCSVM (One-Class SVM), and KNN to synthetic data with known outliers, testing and evaluating the effectiveness of 4 technologies. Finally, the four methods are applied to the detection of outliers in simplified groundwater levels. The results show that in the detection of outliers in synthesized data, the OCSVM method has the best detection performance, with a precision rate of 88.89%, a recall rate of 91.43%, an F1 score of 90.14%, and an AUC value of 95.66%. In the detection of outliers in simplified groundwater levels, a qualitative analysis of the displacement data within the field of view indicates that the outlier detection performance of iForest and OCSVM is better than that of KNN. The proposed method for considering the factors affecting groundwater levels can improve the efficiency and accuracy of detecting outliers in groundwater level data. [ABSTRACT FROM AUTHOR]