The Yangtze River Delta urban agglomeration is the leading and demonstration area for the high-quality development of culture tourism (HDCT) in China. It is of great significance to study the spatiotemporal characteristics and impact mechanism of the HDCT for revealing the internal law of HDCT and promoting the collaborative innovation of culture tourism among cities. Based on the scientific construction of the evaluation system of HDCT, this paper made a quantitative analysis of 26 cities’ HDCT by using coupling coordination degree model, Lisa spatiotemporal transition and spatial Durbin model (SDM). The results show that The overall level of 26 cities’ HDCT shows a fluctuating upward trend, and presents a “Z” pattern in space. More than 80% of the cities are at the medium and high level. Shanghai has obvious advantages in the primacy degree. There is a significant positive spatial autocorrelation among cities with high-quality of culture tourism development. The spatial clustering and proximity of the same ural tourism.

As a consequence of stay-at-home and other lockdown measures, such as social distancing, all health care service provisions during the COVID-19 pandemic have been affected, including the provision of speech therapy. Telehealth services can play a major role in maintaining access to health care, help speech and language pathologists (SLPs) overcome physical barriers by providing patients and caregivers with access to health care, and limit the discontinuity of patient care. To have a better understanding of the changes that have occurred in these services during COVID-19, this research was conducted to explore the nature and current situation of speech-language services in Saudi Arabia based on caregivers’ perspectives. It also investigated whether changes have occurred in these services during the COVID-19 lockdown. The study also determined the perception of caregivers in delivering SLS sessions remotely.

A cross-sectional study was conducted with 385 caregivers in Saudi Arabia. An online survey asked whoption for service delivery, the caregivers showed welcoming responses, particularly with video calls.

The study revealed that SLS services in Saudi Arabia are limited and that accessing these services is challenging. Alternative service delivery using remote services could help caregivers overcome such challenges. When telehealth was introduced as an option for service delivery, the caregivers showed welcoming responses, particularly with video calls.A major challenge for malaria is the lack of tools for accurate and timely diagnosis in the field which are critical for case management and surveillance. Microscopy along with rapid diagnostic tests are the current mainstay for malaria diagnosis in most endemic regions. However, these methods present several limitations. Selleck Etomoxir This study assessed the accuracy of Gazelle, a novel rapid malaria diagnostic device, from samples collected from the Peruvian Amazon between 2019 and 2020. Diagnostic accuracy was compared against microscopy and two rapid diagnostic tests (SD Bioline and BinaxNOW) using 18ssr nested-PCR as reference test. In addition, a real-time PCR assay (PET-PCR) was used for parasite quantification. Out of 217 febrile patients enrolled and tested, 180 specimens (85 P. vivax and 95 negatives) were included in the final analysis. Using nested-PCR as the gold standard, the sensitivity and specificity of Gazelle was 88.2% and 97.9%, respectively. Using a cutoff of 200 parasites/μl, Gazelle’s sensitivity for samples with more than 200 p/uL was 98.67% (95%CI 92.79% to 99.97%) whereas the sensitivity for samples lower than 200 p/uL (n = 10) was 12.5% (95%CI 0.32% to 52.65%). Gazelle’s sensitivity and specificity were statistically similar to microscopy (sensitivity = 91.8, specificity = 100%, p = 0.983) and higher than both SD Bioline (sensitivity = 82.4, specificity = 100%, p = 0.016) and BinaxNOW (sensitivity = 71.8%, specificity = 97.9%, p = 0.002). The diagnostic accuracy of Gazelle for malaria detection in P. vivax infections was comparable to light microscopy and superior to both RDTs even in the presence of low parasitemia infections. The performance of Gazelle makes it a valuable tool for malaria diagnosis and active case detection that can be utilized in different malaria-endemic regions.Cytokinins (CKs) plays a key role in plant adaptation over a range of different stress conditions. Here, we analyze the effects of a cytokinin (i.e., kinetin, KN) on the growth, photosynthesis (rate of O2 evolution), PS II photochemistry and AsA-GSH cycle in Trigonella seedlings grown under cadmium (Cd) stress. Trigonella seeds were sown in soil amended with 0, 3 and 9 mg Cd kg-1 soil, and after 15 days resultant seedlings were sprayed with three doses of KN, i.e.,10 μM (low, KNL), 50 μM (medium, KNM) and 100 μM (high, KNH); subsequent experiments were performed after 15 days of KN application, i.e., 30 days after sowing. Cadmium toxicity induced oxidative damage as shown by decreased seedling growth and photosynthetic pigment production (Chl a, Chl b and Car), rates of O2-evolution, and photochemistry of PS II of Trigonella seedlings, all accompanied by an increase in H2O2 accumulation. Supplementation with doses of KN at KNL and KNM significantly improved the growth and photosynthetic activity by reducing H2O2 accumulation through the up-regulation AsA-GSH cycle. Notably, KNL and KNM doses stimulated the rate of enzyme activities of APX, GR and DHAR, involved in the AsA-GSH cycle thereby efficiently regulates the level of AsA and GSH in Trigonella grown under Cd stress. The study concludes that KN can mitigate the damaging effects of Cd stress on plant growth by maintaining the redox status (>ratios AsA/DHA and GSH/GSSG) of cells through the regulation of AsA-GSH cycle at 10 and 50 μM KN under Cd stress conditions. At 100 μM KN, the down-regulation of AsA-GSH cycle did not support the growth and PS II activity of the test seedlings.Convolutional neural networks (CNNs) are widely used for artificial intelligence (AI)-based image classification. Residual network (ResNet) is a new technology that facilitates the accuracy of image classification by CNN-based AI. In this study, we developed a novel AI model combined with ResNet to diagnose colorectal polyps. In total, 127,610 images consisting of 62,510 images with adenomatous polyps, 30,443 with non-adenomatous hyperplastic polyps, and 34,657 with healthy colorectal normal mucosa were subjected to deep learning after annotation. Each validation process was performed using 12,761 stored images of colorectal polyps by a 10-fold cross validation. The efficacy of the ResNet system was evaluated by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy for adenomatous polyps at WLIs were 98.8%, 94.3%, 90.5%, 87.4%, and 92.8%, respectively. Similar results were obtained for adenomatous polyps at narrow-band imagings (NBIs) and chromoendoscopy images (CEIs) (NBIs vs.