Correction: Motivating factors for physical activity participation among individuals with chronic obstructive pulmonary disease: A qualitative study applying the motivation, opportunity, and ability model.

[This corrects the article DOI: 10.1371/journal.pone.0303858.].

Motivating factors for physical activity participation among individuals with chronic obstructive pulmonary disease: A qualitative study applying the motivation, opportunity, and ability model.

OBJECTIVE: The study aims to explore the driving forces behind physical activity engagement among patients with chronic obstructive pulmonary disease, focusing on motivation, opportunity, and capability. DESIGN: A phenomenological qualitative study applied the motivation, opportunity, and capability model, conducted in two respiratory units of a Chinese university hospital. METHODS: Participants, selected by age, gender, and illness duration, included inpatients during the interview sessions and those recently discharged within six months. One-on-one semi-structured interviews were recorded, transcribed, and analyzed by the Colaizzi seven-step method. RESULTS: Seventeen participants diagnosed with chronic obstructive pulmonary disease for over one year aged between 66 (range: 42-96) participated. Three major themes were identified: Inspiring participation motivation-transitioning from recognizing significance to habit formation; Offering participation opportunities-reiterating demand for personalized strategies and ideal environmental settings; Enhancing participation capability-addressing strategies for overcoming fears, setting goals, ensuring safety, and adjusting activity levels. CONCLUSIONS: This research underscores the vital role of inspiring participation motivation, offering opportunities, and enhancing the capability for participation in effective engagement. Advocating increased attention from healthcare departments, fostering interdisciplinary collaboration, improving activity guidance and counseling effectiveness, and considering individual preferences can significantly benefit those patients with chronic obstructive pulmonary disease who hesitate or are unable to participate in physical activities, thereby increasing the dose of non-leisure time physical activity.

An optical multiple-image authentication based on computational ghost imaging and total-variation minimization.

An optical multiple-image authentication is suggested using computational ghost imaging and total-variation minimization. Differing from encrypting multiple images into a noise-like ciphertext directly, as described in most conventional authentication methods, the related encoded information is embedded into a cover image to avoid the attention of eavesdroppers. First, multiple images are encoded to form real-valued sequences composed of corresponding bucket values obtained by the aid of computational ghost imaging, and four sub-images are obtained by decomposing the cover image using wavelet transform. Second, measured sequences are embedded into one of the sub-images, and embedding positions are randomly selected using corresponding binary masks. To enhance the security level, a chaotic sequence is produced using logistic map and used to scramble measured intensities. Most importantly, original images with high quality can be directly recovered using total-variation minimization. The validity and robustness of the proposed approach are verified with optical experiments.

Optical multiple-image authentication based on computational ghost imaging and hybrid non-convex second-order total variation.

An optical security method for multiple-image authentication is proposed based on computational ghost imaging and hybrid non-convex second-order total variation. Firstly, each original image to be authenticated is encoded to the sparse information using computational ghost imaging, where illumination patterns are generated based on Hadamard matrix. In the same time, the cover image is divided into four sub-images with wavelet transform. Secondly, one of sub-images with low-frequency coefficients is decomposed using singular value decomposition (SVD), and all sparse data are embedded into the diagonal matrix with the help of binary masks. To enhance the security, the generalized Arnold transform is used to scramble the modified diagonal matrix. After using SVD again, the marked cover image carrying the information of multiple original images is obtained using the inverse wavelet transform. In the authentication process, the quality of each reconstructed image can be greatly improved based on hybrid non-convex second-order total variation. Even at a very low sampling ratio (i.e., 6%), the existence of original images can be efficiently verified using the nonlinear correlation maps. To our knowledge, it is first to embed sparse data into the high-frequency sub-image using two cascaded SVDs, which can guarantee high robustness against the Gaussian filter and sharpen filter. The optical experiments demonstrate the feasibility of the proposed mechanism, which can provide an effective alternative for the multiple-image authentication.

Switchable enzyme mimics based on self-assembled peptides for polyethylene terephthalate degradation.

Polyethylene terephthalate (PET), the most abundant polyester plastic, has become a global concern due to its refractoriness and accumulation in the environment. In this study, inspired by the structure and catalytic mechanism of the native enzyme, peptides, based on supramolecular self-assembly, were developed to construct enzyme mimics for PET degradation, which were achieved by combining the enzymatic active sites of serine, histidine and aspartate with the self-assembling polypeptide MAX. The two designed peptides with differences in hydrophobic residues at two positions exhibited a conformational transition from random coil to ß-sheet by changing the pH and temperature, and the catalytic activity followed the self-assembly "switch" with the fibrils formed ß-sheet, which could catalyze PET efficiently. Although the two peptides possessed same catalytic site, they showed different catalytic activities. Analysis of the structure - activity relationship of the enzyme mimics suggested that the high catalytic activity of the enzyme mimics for PET could be attributed to the formation of stable fibers of peptides and ordered arrangement of molecular conformation; in addition, hydrogen bonding and hydrophobic interactions, as the major forces, promoted effects of enzyme mimics on PET degradation. Enzyme mimics with PET-hydrolytic activity are a promising material for degrading PET and reducing environmental pollution.

Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review.

Objective: The objectives of this systematic review are to describe features and specific application scenarios for current cancer survivorship care services of Artificial intelligence (AI)-driven digital health technologies (DHTs) and to explore the acceptance and briefly evaluate its feasibility in the application process. Methods: Search for literatures published from 2010 to 2022 on sites MEDLINE, IEEE-Xplor, PubMed, Embase, Cochrane Central Register of Controlled Trials and Scopus systematically. The types of literatures include original research, descriptive study, randomized controlled trial, pilot study, and feasible or acceptable study. The literatures above described current status and effectiveness of digital medical technologies based on AI and used in cancer survivorship care services. Additionally, we use QuADS quality assessment tool to evaluate the quality of literatures included in this review. Results: 43 studies that met the inclusion criteria were analyzed and qualitatively synthesized. The current status and results related to the application of AI-driven DHTs in cancer survivorship care were reviewed. Most of these studies were designed specifically for breast cancer survivors' care and focused on the areas of recurrence or secondary cancer prediction, clinical decision support, cancer survivability prediction, population or treatment stratified, anti-cancer treatment-induced adverse reaction prediction, and so on. Applying AI-based DHTs to cancer survivors actually has shown some positive outcomes, including increased motivation of patient-reported outcomes (PROs), reduce fatigue and pain levels, improved quality of life, and physical function. However, current research mostly explored the technology development and formation (testing) phases, with limited-scale population, and single-center trial. Therefore, it is not suitable to draw conclusions that the effectiveness of AI-based DHTs in supportive cancer care, as most of applications are still in the early stage of development and feasibility testing. Conclusions: While digital therapies are promising in the care of cancer patients, more high-quality studies are still needed in the future to demonstrate the effectiveness of digital therapies in cancer care. Studies should explore how to develop uniform standards for measuring patient-related outcomes, ensure the scientific validity of research methods, and emphasize patient and health practitioner involvement in the development and use of technology.