The acceptance and hesitancy of COVID-19 vaccination among chronic obstructive pulmonary disease (COPD) patients.

Severe acute respiratory syndrome coronavirus 2, which is responsible for the coronavirus disease 2019 (COVID-19), causes severe clinical outcomes in old individuals and patients with underlying diseases, including chronic obstructive pulmonary disease (COPD). Considering vaccination is still the most effective method to prevent COVID-19-associated death, it is imperative to evaluate COPD patients' attitudes toward the COVID-19 vaccine. This cross-sectional design study was conducted to assess vaccine acceptance and hesitancy among 212 COPD patients who attended the outpatient department from January 1, 2021, to July 31, 2022. All of the patients were not vaccinated and had undertaken lung function test at the time of our survey. Of 212 participants, 164 (77.4%) were willing to be vaccinated immediately while 48 (22.6%) were hesitant to be vaccinated. Compared with the acceptance group, patients who did not accept the vaccination instantly tended to have more comorbidities, like hypertension, coronary heart disease, recent cancers, and higher Modified British Medical Research Council score, or more frequent acute exacerbation. For the patients willing to be vaccinated, the main factors motivating them were an authorities-endorsed vaccine, free vaccination, and no obvious adverse reactions. For the hesitant group, no recommendation from the treating physician was the biggest obstacle for them to accept vaccination. Our results provide useful guidance for making intervention measures to enhance COPD patients' acceptance of a new COVID-19 vaccine. For those patients with comorbidities, treating physicians promoting messages framing the safety of vaccination is necessary to increase immunization rates.

Tailoring the Asymmetric Structure of NH2 -UiO-66 Metal-Organic Frameworks for Light-promoted Selective and Efficient Gold Extraction and Separation.

Designing adsorption materials with high adsorption capacities and selectivities is highly desirable for precious metal recovery. Desorption performance is also particularly crucial for subsequent precious metal recovery and adsorbent regeneration. Herein, a metal-organic framework (MOF) material (NH2 -UiO-66) with an asymmetric electronic structure of the central zirconium oxygen cluster has an exceptional gold extraction capacity of 2.04 g g-1 under light irradiation. The selectivity of NH2 -UiO-66 for gold ions is up to 98.8 % in the presence of interfering ions. Interestingly, the gold ions adsorbed on the surface of NH2 -UiO-66 spontaneously reduce in situ, undergo nucleation and growth and finally achieve the phase separation of high-purity gold particles from NH2 -UiO-66. The desorption and separation efficiency of gold particles from the adsorbent surface reaches 89 %. Theoretical calculations indicate that -NH2 functions as a dual donor of electrons and protons, and the asymmetric structure of NH2 -UiO-66 leads to energetically advantageous multinuclear gold capture and desorption. This adsorption material can greatly facilitate the recovery of gold from wastewater and can easily realize the recycling of the adsorbent.

Resource Recovery Strategy of Rapid Screening Kits for Diseases through Ozonation.

Rapid disease screening kits are widely used due to their efficiency and convenience in disease screening. However, the resulting medical waste poses a significant environmental burden. Recycling this waste in a resourceful manner not only protects the environment from pollution but also maximizes savings in resources and energy consumption. In this study, we developed a scalable, simple, and efficient method for the resourceful recovery of assay kits. The method achieves simultaneous 100% pathogen inactivation in the kits, complete leaching of gold nanoparticles (Au NPs), and nondestructive recovery of plastics and tampons. Additionally, our mechanistic study on Au leaching has demonstrated that water plays a crucial role as the initiator for the Au dissolution reaction, and the hydroxyl radical (•OH) serves as an important reactive oxygen species. This research introduces a novel approach for dissolving the precious metal gold and provides a viable technology for the resource utilization of waste disease screening kits.

Enhancement of spin noise spectroscopy of rubidium atomic ensemble by using the polarization squeezed light.

We measured the spin noise spectroscopy (SNS) of rubidium atomic ensemble with two different kinds of atomic vapor cells (filled with buffer gas or coated with paraffin film on the inner wall) and demonstrated the enhancement of the signal-to-noise ratio (SNR) by using polarization squeezed state (PSS) of 795-nm light field with Stokes operator S Λ 2 squeezed. The PSS is prepared by locking the relative phase between the squeezed vacuum state of light obtained with a sub-threshold optical parametric oscillator and the orthogonally polarized local oscillator beam by means of the quantum noise lock. Under the same conditions, the PSS can be employed not only to improve the SNR, but also to keep the full width at half maximum (FWHM) of SNS, compared with the case of using the polarization coherent state (PCS), enhancement of SNR is positively correlated with the squeezing level of the PSS. With increasing probe laser power and atomic number density, the SNR and FWHM of SNS will increase correspondingly. With the help of the PSS of the Stokes operator S Λ 2, quantum improvements of both the SNR and FWHM of SNS signal has been demonstrated by controlling optical power of polarization squeezed light beam or atomic number density in our experiments.

SGC-VSLAM: A Semantic and Geometric Constraints VSLAM for Dynamic Indoor Environments.

As one of the core technologies for autonomous mobile robots, Visual Simultaneous Localization and Mapping (VSLAM) has been widely researched in recent years. However, most state-of-the-art VSLAM adopts a strong scene rigidity assumption for analytical convenience, which limits the utility of these algorithms for real-world environments with independent dynamic objects. Hence, this paper presents a semantic and geometric constraints VSLAM (SGC-VSLAM), which is built on the RGB-D mode of ORB-SLAM2 with the addition of dynamic detection and static point cloud map construction modules. In detail, a novel improved quadtree-based method was adopted for SGC-VSLAM to enhance the performance of the feature extractor in ORB-SLAM (Oriented FAST and Rotated BRIEF-SLAM). Moreover, a new dynamic feature detection method called semantic and geometric constraints was proposed, which provided a robust and fast way to filter dynamic features. The semantic bounding box generated by YOLO v3 (You Only Look Once, v3) was used to calculate a more accurate fundamental matrix between adjacent frames, which was then used to filter all of the truly dynamic features. Finally, a static point cloud was estimated by using a new drawing key frame selection strategy. Experiments on the public TUM RGB-D (Red-Green-Blue Depth) dataset were conducted to evaluate the proposed approach. This evaluation revealed that the proposed SGC-VSLAM can effectively improve the positioning accuracy of the ORB-SLAM2 system in high-dynamic scenarios and was also able to build a map with the static parts of the real environment, which has long-term application value for autonomous mobile robots.