Association of hair cortisol concentration with brain-derived neurotrophic factor gene methylation: The role of sex as a moderator.

Hair cortisol concentration (HCC) reflects the long-term activity of the hypothalamus-pituitary-adrenal (HPA) axis in response to stress. Brain-derived neurotrophic factor DNA methylation (BDNF DNAM) may affect HCC, and sex and Val66Met may contribute to this association. Thus, the aim of this study was to investigate the associations between HCC and Brain-derived neurotrophic factor (BDNF) DNAM, and the moderating effects of Val66Met and sex. We recruited 191 healthy young participants (96 women, mean age 23.0 ± 2.6 years) and collected body samples to evaluate HCC, and to determine BDNF DNAM and Val66Met genotypes. We analyzed the effects of BDNF DNAM, sex, and Val66Met on HCC. We also evaluated the associations between BDNF DNAM and HCC in groups separated by sex and genotypes. We found a marked association of BDNF DNAM with HCC across men and women. After dividing the data by sex, a positive correlation of HCC with BDNF DNAM was found only in women. There was no substantial moderation effect of Val66Met genotypes on the association between BDNF DNAM and HCC. Therefore, BDNF DNAM was found to have positive association with HCC only in healthy young women, indicating that sex moderates the association of BDNF DNAM with long-term HPA axis activity.

Decorating Pd-Au Nanodots Around Porous In2 O3 Nanocubes for Tolerant H2 Sensing Against Switching Response and H2 S Poisoning.

With the recently-booming hydrogen (H2 ) economy by green H2 as the energy carriers and the newly-emerged exhaled diagnosis by human organ-metabolized H2 as a biomarker, H2 sensing is simultaneously required with fast response, low detection limit, and tolerant stability against humidity, switching, and poisoning. Here, reliable H2 sensing has been developed by utilizing indium oxide nanocubes decorated with palladium and gold nanodots (Pd-Au NDs/In2 O3 NCBs), which have been synthesized by combined hydrothermal reaction, annealing, and chemical bath deposition. As-prepared Pd-Au NDs/In2 O3 NCBs are observed with surface-enriched NDs and nanopores. Beneficially, Pd-Au NDs/In2 O3 NCBs show 300 ppb-low detection limit, 5 s-fast response to 500 ppm H2 , 75%RH-high humidity tolerance, and 56 days-long stability at 280 °C. Further, Pd-Au NDs/In2 O3 NCBs show excellent stability against switching sensing response, and are tolerant to H2 S poisoning even being exposed to 10 ppm H2 S at 280 °C. Such excellent H2 sensing may be attributed to the synergistic effect of the boosted Pd-Au NDs' spillover effect and interfacial electron transfer, increased adsorption sites over the porous NCBs' surface, and utilized Pd NDs' affinity with H2 and H2 S. Practically, Pd-Au NDs/In2 O3 NCBs are integrated into the H2 sensing device, which can reliably communicate with a smartphone.

Two-dimensional Aluminum Oxide Nanosheets Decorated with Palladium Oxide Nanodots for Highly Stable and Selective Hydrogen Sensing.

Hydrogen (H2 ) sensing materials such as semiconductor metal oxides may suffer from poor long-term stability against humidity and unsatisfactory selectivity against other interfering gases. To address the above issues, highly stable and selective H2 sensing built with palladium oxide nanodots decorating aluminum oxide nanosheets (PdO NDs//Al2 O3 NSs) has been achieved via combined template synthesis, photochemical deposition, and oxidation. Typically, the PdO NDs//Al2 O3 NSs are observed with thin NSs (≈17 nm thick) decorated with nanodots (≈3.3 nm in diameter). Beneficially, the sensor prototypes built with PdO NDs//Al2 O3 NSs show excellent long-term stability for 278 days, high selectivity against interfering gases, and outstanding stability against humidity at 300 °C. Remarkably, the sensor prototypes enable detection of a wide-range of 20 ppm - 6 V/V% H2 , and the response and recovery times are ≈5 and 16 s to 1 V/V% H2 , respectively. Theoretically, the heterojunctions of PdO NDs-Al2 O3 NSs with a large specific surface ratio and Al2 O3 NSs as the support exhibit excellent stability and selective H2 sensing. Practically, a sensing device integrated with the PdO NDs//Al2 O3 NSs sensor prototype is simulated for detecting H2 with reliable sensing response.

Can fat infiltration in the multifidus muscle be a predictor of postoperative symptoms and complications in patients undergoing lumbar fusion for degenerative lumbar spinal stenosis? A case-control study.

PURPOSE: This study aimed to explore whether 25% as the cutoff value of fat infiltration (FI) in multifidus (MF) could be a predictor of clinical outcomes of lumbar spinal stenosis (LSS) patients. METHODS: A total of 461 patients undergoing posterior lumbar interbody fusion for LSS with 1-year follow-up were identified. After sex- and age-match, 160 pairs of patients were divided into a FI < 25% group and a FI ≥ 25% group according to FI of MF at L4 on preoperative magnetic resonance imaging. Patient-reported outcomes including the visual analog scale scores (VAS) for back pain and leg pain and the Oswestry disability index (ODI) scores were evaluated. Bone nonunion and screw loosening were evaluated by dynamic X-ray. RESULTS: After matching, there was no significant difference in age, sex, body mass index, fusion to S1, number of fusion levels, osteoporosis, spondylolisthesis, smoking and diabetes. FI ≥ 25% group had significantly higher VAS for back pain, VAS for leg pain and ODI than FI < 25% group at 1-year follow-up. However, there was no significant difference in the change of them from baseline to 1-year follow-up between the two groups. In light of complications, FI ≥ 25% group had a significantly higher rate of bone nonunion than FI < 25% group, whereas there was no significant difference of screw loosening rates between the two groups. CONCLUSION: MF FI might be a pragmatic cutoff value to predict bone nonunion in LSS patients, but it has little predictive value on screw loosening and postoperative improvement of symptoms.

Three-Dimensional Porous Carbon/Nitrogen Framework-Decorated Palladium Nanoparticles for Stable and Wide-Concentration-Range Hydrogen Sensing.

Hydrogen (H2) as a high-energy-density carrier is of great potential in the upcoming hydrogen economy. Nevertheless, H2/air mixtures are explosive at H2 concentrations above 4 v/v % and reliable and wide-concentration-range H2 sensors are thus highly desired. Here, hydrogen sensing has been developed using palladium nanoparticles of ∼11.2 nm in diameter chemically decorated on the carbon/nitrogen three-dimensional porous framework of 308 m2 g-1 in specific surface area (Pd NPs@CN 3D framework). Theoretically, the Pd NPs and CN 3D framework are used to construct the Mott-Schottky heterojunctions, in which the CN 3D framework possesses a higher work function, promoting electron transfer to Pd NPs and therefore highly active dissociation of H2. Beneficially, the Pd NPs@CN 3D framework exhibits a wide concentration range of 200 ppm (S ≈ 0.2% and Tres ≈ 15 s) to 40 v/v % (S ≈ 73.8% and Tres ≈ 9 s) H2 sensing at room temperature. Remarkably, the H2 sensor prototype built with the Pd NPs@CN 3D framework shows excellent long-term stability that maintains reliable H2 sensing after 142 days. Such stable hydrogen sensing provides an experimental basis for the wide-concentration-range detection of H2 leakage in the future hydrogen economy.

Radial ZnO nanorods decorating Co3O4 nanoparticles for highly selective and sensitive detection of the 3-hydroxy-2-butanone biomarker.

Indirect monitoring of Listeria monocytogenes (LM) via a gas sensor that can detect the bacterial metabolite 3-hydroxy-2-butanone (3H-2B) is a newly emerged strategy. However, such sensors are required simultaneously endow with outstanding selectivity, high sensitivity, and ppb-level detection limit, which remains technologically challenging. Herein, we have developed highly selective and sensitive 3H-2B sensors that consist of zinc oxide nanorods decorated with cobaltosic oxide nanoparticles (ZnO NRs/Co3O4 NPs), which have been synthesized by combined optimized hydrothermal and annealing process. Specifically, the ZnO NRs/Co3O4 NPs exhibit ultrahigh sensitivity to 5 ppm 3H-2B (Ra/Rg = 550 at 260 °C). The sensor prototypes enable detection as low as 10 ppb 3H-2B, show excellent long-term stability, and present remarkable selectivity through interfering selectivity survey and principal component analysis (PCA). Such outstanding sensing performance is attributed to the modulated electron depletion layer by n-p heterojunctions and abundant gas diffusion pathways via the radial architecture, which was verified via electrochemical impedance spectroscopy test, Mott-Schottky measurement, and ultraviolet-visible absorption analysis. Our highly selective and sensitive ZnO NRs/Co3O4 NPs have the potential in the real-time detection of 3H-2B biomarker.

Highly sensitive and fast-response hydrogen sensing of WO3 nanoparticles via palladium reined spillover effect.

Hydrogen sensing simultaneously endowed with fast response, high sensitivity and selectivity is highly desired in detecting hydrogen leakages such as in hydrogen-driven vehicles and space rockets. Here, hydrogen sensing reined via a hydrogen spillover effect has been developed using palladium nanoparticles photochemically decorated on WO3 nanoparticles (Pd-NPs@WO3-NPs). Theoretically, the Pd-NP catalysts and WO3-NP support are used to construct the hydrogen spillover system, in which Pd NPs possess high catalytic activity, promoting the electron transfer and therefore the reaction kinetics. Beneficially, the Pd-NPs@WO3-NP sensor prototypes toward 500 ppm hydrogen simultaneously exhibit fast response time (∼1.2 s), high response (Ra/Rg = 22 867) and selectivity at a working temperature of 50 °C. Such advanced hydrogen sensing provides an experimental basis for the smart detection of hydrogen leakage in the future hydrogen economy.

Road Extraction from Unmanned Aerial Vehicle Remote Sensing Images Based on Improved Neural Networks.

Roads are vital components of infrastructure, the extraction of which has become a topic of significant interest in the field of remote sensing. Because deep learning has been a popular method in image processing and information extraction, researchers have paid more attention to extracting road using neural networks. This article proposes the improvement of neural networks to extract roads from Unmanned Aerial Vehicle (UAV) remote sensing images. D-Linknet was first considered for its high performance; however, the huge scale of the net reduced computational efficiency. With a focus on the low computational efficiency problem of the popular D-LinkNet, this article made some improvements: (1) Replace the initial block with a stem block. (2) Rebuild the entire network based on ResNet units with a new structure, allowing for the construction of an improved neural network D-Linknetplus. (3) Add a 1 × 1 convolution layer before DBlock to reduce the input feature maps, reducing parameters and improving computational efficiency. Add another 1 × 1 convolution layer after DBlock to recover the required number of output channels. Accordingly, another improved neural network B-D-LinknetPlus was built. Comparisons were performed between the neural nets, and the verification were made with the Massachusetts Roads Dataset. The results show improved neural networks are helpful in reducing the network size and developing the precision needed for road extraction.