Suppressing STAT3 activation impairs bone formation during maxillary expansion and relapse

Objectives: The mid-palatal expansion technique is commonly used to correct maxillary constriction in dental clinics. However, there is a tendency for it to relapse, and the key molecules responsible for modulating bone formation remain elusive. Thus, this study aimed to investigate whether signal transducer and activator of transcription 3 (STAT3) activation contributes to osteoblast-mediated bone formation during palatal expansion and relapse. Methodology: In total, 30 male Wistar rats were randomly allocated into Ctrl (control), E (expansion only), and E+Stattic (expansion plus STAT3-inhibitor, Stattic) groups. Micro-computed tomography, micromorphology staining, and immunohistochemistry of the mid-palatal suture were performed on days 7 and 14. In vitro cyclic tensile stress (10% magnitude, 0.5 Hz frequency, and 24 h duration) was applied to rat primary osteoblasts and Stattic was administered for STAT3 inhibition. The role of STAT3 in mechanical loading-induced osteoblasts was confirmed by alkaline phosphatase (ALP), alizarin red staining, and western blots. Results: The E group showed greater arch width than the E+Stattic group after expansion. The differences between the two groups remained significant after relapse. We found active bone formation in the E group with increased expression of ALP, COL-I, and Runx2, although the expression of osteogenesis-related factors was downregulated in the E+stattic group. After STAT3 inhibition, expansive force-induced bone resorption was attenuated, as TRAP staining demonstrated. Furthermore, the administration of Stattic in vitro partially suppressed tensile stress-enhanced osteogenic markers in osteoblasts. Conclusions: STAT3 inactivation reduced osteoblast-mediated bone formation during palatal expansion and post-expansion relapse, thus it may be a potential therapeutic target to treat force-induced bone formation

A radiative cooling structural material.

Reducing human reliance on energy-inefficient cooling methods such as air conditioning would have a large impact on the global energy landscape. By a process of complete delignification and densification of wood, we developed a structural material with a mechanical strength of 404.3 megapascals, more than eight times that of natural wood. The cellulose nanofibers in our engineered material backscatter solar radiation and emit strongly in mid-infrared wavelengths, resulting in continuous subambient cooling during both day and night. We model the potential impact of our cooling wood and find energy savings between 20 and 60%, which is most pronounced in hot and dry climates

Polymorphisms in thymidylate synthase gene and susceptibility to breast cancer in a Chinese population: a case-control analysis

BACKGROUND: Accumulative evidence suggests that low folate intake is associated with increased risk of breast cancer. Polymorphisms in genes involved in folate metabolism may influence DNA methylation, nucleotide synthesis, and thus individual susceptibility to cancer. Thymidylate synthase (TYMS) is a key enzyme that participates in folate metabolism and catalyzes the conversion of dUMP to dTMP in the process of DNA synthesis. Two potentially functional polymorphisms [a 28-bp tandem repeat in the TYMS 5'-untranslated enhanced region (TSER) and a 6-bp deletion/insertion in the TYMS 3'-untranslated region (TS 3'-UTR)] were suggested to be correlated with alteration of thymidylate synthase expression and associated with cancer risk. METHODS: To test the hypothesis that polymorphisms of the TYMS gene are associated with risk of breast cancer, we genotyped these two polymorphisms in a case-control study of 432 incident cases with invasive breast cancer and 473 cancer-free controls in a Chinese population. RESULTS: We found that the distribution of TS3'-UTR (1494del6) genotype frequencies were significantly different between the cases and controls (P = 0.026). Compared with the TS3'-UTR del6/del6 wild-type genotype, a significantly reduced risk was associated with the ins6/ins6 homozygous variant genotype (adjusted OR = 0.58, 95% CI = 0.35–0.97) but not the del6/ins6 genotype (OR = 1.09, 95% CI = 0.82–1.46). Furthermore, breast cancer risks associated with the TS3'-UTR del6/del6 genotype were more evident in older women, postmenopausal subjects, individuals with a younger age at first-live birth and individuals with an older age at menarche. However, there was no evidence for an association between the TSER polymorphism and breast cancer risks. CONCLUSION: These findings suggest that the TS3'-UTR del6 polymorphism may play a role in the etiology of breast cancer. Further larger population-based studies as well as functional evaluation of the variants are warranted to confirm our findings

Corrigendum to: The TianQin project: current progress on science and technology

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article

A new sufficient schedulability analysis for hybrid scheduling

Earliest deadline first (EDF) and fixed priority (FP) are the most commonly used and studied scheduling algorithms for real-time systems. This paper focuses on combining the EDF and FP strategies in one system. We provide a new sufficient schedulability analysis for real-time hybrid task systems which are scheduled by EDF and FP. The proposed analysis has a polynomial time complexity and no restrictions on task parameters, where the relative deadline of each task could be less than, equal to, or greater than its period. By extensive experiments, we show that our proposed analysis significantly improves the acceptance ratio compared with the existing results of the sufficient schedulability test for hybrid scheduling systems

Mutual Generative Transformer Learning for Cross-view Geo-localization

Cross-view geo-localization (CVGL), which aims to estimate the geographical location of the ground-level camera by matching against enormous geo-tagged aerial (e.g., satellite) images, remains extremely challenging due to the drastic appearance differences across views. Existing methods mainly employ Siamese-like CNNs to extract global descriptors without examining the mutual benefits between the two modes. In this paper, we present a novel approach using cross-modal knowledge generative tactics in combination with transformer, namely mutual generative transformer learning (MGTL), for CVGL. Specifically, MGTL develops two separate generative modules--one for aerial-like knowledge generation from ground-level semantic information and vice versa--and fully exploits their mutual benefits through the attention mechanism. Experiments on challenging public benchmarks, CVACT and CVUSA, demonstrate the effectiveness of the proposed method compared to the existing state-of-the-art models

Experimental Study of a New Pneumatic Actuating System Using Exhaust Recycling

Pneumatic actuating systems are an important power system in industrial applications. Due to exhaust loss, however, pneumatic actuating systems have suffered from a low utilization of compressed air. To recycle the exhaust energy, a novel pneumatic circuit was proposed to realize energy savings through recycling exhaust energy. The circuit consisted of three two-position three-way switch valves, which were used to control the exhaust flows into a gas tank or the ambient environment. This paper introduced the energy recovery configuration and working principles and built a mathematical model of its working process. Then, the mathematical model was verified by experiments. Finally, through experiments in which the air supply pressure, the critical pressure and the volume of the gas tank were regulated, the energy recovery characteristics of the pneumatic actuating system were obtained. Using the new circuit, the experimental results showed that the energy recovery efficiency exceeded 23%. When the air supply pressure was set to 5 bar, 6 bar, and 7 bar, the time required for pneumatic actuation to complete the three working cycles were 5.2 s, 5.3 s, and 5.9 s, respectively. When the critical pressure was set to 0 bar, 0.5 bar, 1 bar, and 1.5 bar, the times for pneumatic actuation to complete the three working cycles were 4.9 s, 5.1 s, 5.2 s, and 5.3 s, respectively. When the volume of the gas tank was set to 2 L, 3 L, 4 L, and 5 L, the number of working cycles was 3, 4, 5, and 6, respectively. This paper provides a new method of cylinder exhaust recycling and lays a good foundation for pneumatic energy savings

Co-Visual Pattern-Augmented Generative Transformer Learning for Automobile Geo-Localization

Geolocation is a fundamental component of route planning and navigation for unmanned vehicles, but GNSS-based geolocation fails under denial-of-service conditions. Cross-view geo-localization (CVGL), which aims to estimate the geographic location of the ground-level camera by matching against enormous geo-tagged aerial (e.g., satellite) images, has received a lot of attention but remains extremely challenging due to the drastic appearance differences across aerial–ground views. In existing methods, global representations of different views are extracted primarily using Siamese-like architectures, but their interactive benefits are seldom taken into account. In this paper, we present a novel approach using cross-view knowledge generative techniques in combination with transformers, namely mutual generative transformer learning (MGTL), for CVGL. Specifically, by taking the initial representations produced by the backbone network, MGTL develops two separate generative sub-modules—one for aerial-aware knowledge generation from ground-view semantics and vice versa—and fully exploits the entirely mutual benefits through the attention mechanism. Moreover, to better capture the co-visual relationships between aerial and ground views, we introduce a cascaded attention masking algorithm to further boost accuracy. Extensive experiments on challenging public benchmarks, i.e., CVACT and CVUSA, demonstrate the effectiveness of the proposed method, which sets new records compared with the existing state-of-the-art models. Our code will be available upon acceptance