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Background: Cold environments pose serious threats on human health, with increased risk for myocardial infarction, stroke, frostbite, and hypothermia. Acquired cold acclimation is required to minimize cold-induced injures and to improve metabolic health. However, the underlying mechanisms remain to be fully elucidated. Objective: We aimed to identify critical amino acids involved in cold acclimation and unmask the regulatory mechanisms. Methods: A total of twenty male participants were recruited and followed up after 3 months’ natural cold exposure. Cold-induced vasodilation (CIVD) tests and clinical biochemical analysis were performed at baseline and after 3-months cold exposure, whilst blood samples were collected, and plasma amino acids were analyzed by targeted metabolomics. To further confirm the effect of lysine on cold tolerance and explain the latent mechanism, mice were challenged with chronic cold exposure for 7 days with lysine supplement, then core and local surface temperature as well as thermogenesis activity were detected. Results: Continuous cold exposure shortened the CIVD onset time and increased the average finger temperature. Levels of the plasma lysine and glycine were decreased in both humans and mice. Venn analysis from three datasets revealed that lysine was the only significantly changed plasma amino acid, which strongly correlated with the altered CIVD. Moreover, mice sustained a relatively higher core temperature and surface temperature in the back, tail and paws upon lysine supplementation. Furthermore, lysine supplementation increased the level of histone H3K18cr and promoted the gene and protein expression of Cpt1a, Cpt2 and Cyp27a1 in liver. Conclusion: Our work identified lysine as a critical amino acid for the remodeling of hepatic histone crotonylation that facilitates cold acclimation.

This paper examines the chaos caused by introducing advanced language models, specifically ChatGPT, to art. Our focus is on the potential impact of ChatGPT on art creation and collaboration. We explore how it has been utilized to generate art and assist in creative writing and how it facilitates collaboration between artists. This exploration includes an investigation into the use of AI in creating art, music, and literature, emphasizing ChatGPT’s role in generating poetry and prose and its ability to provide valuable suggestions for sentence structure and word choice in creative writing. We conduct case studies and interviews with diverse artists and AI experts to understand the benefits and challenges of using ChatGPT in the creative process. Our findings reveal that artists find ChatGPT helpful in generating new ideas, overcoming creative blocks, and improving the quality of their work. It enables remote collaboration between artists by providing a real-time communication and idea-sharing platform. However, ethical concerns relating to authorship ownership and authenticity have emerged. Artists fear using ChatGPT may lead to losing their artistic identity and ownership of their work. While our data suggests that ChatGPT holds the potential to transform the art world, careful consideration must be given to the ethical implications of AI in art. We recommend future research to focus on developing guidelines for the responsible use of AI in art, safeguarding artists’ rights, and preserving artistic authenticity.

Wetlands are transitional zones between terrestrial and aquatic ecosystems. They have the potential to continuously provide human beings with food, raw materials, and other substances. Also, wetland landscape pattern changes have profound impacts on the climate of plateau areas and accelerate the rate of climate change, so it is crucial to extract long time series of plateau wetland information. Concurrent with large-scale urbanization, industrial development and construction, and rapid population increases, the lakeside wetlands in the Dianchi Lake Basin are changing rapidly. However, scholars have not yet extracted long time series wetland information for this area, so the long-term evolution of these wetlands cannot be clarified. In this study, we used the Google Earth Engine (GEE) platform to extract wetland information for the Dianchi Basin from 1988 to 2020 from Landsat data and generated 33 wetland maps by applying a random forest classification model to identify trends and a confusion matrix to assess accuracy. The results showed that the overall trend of wetland area from 1988 to 2020 showed an increasing area, and the total wetland area increased by 31.11 km2 (+9.67 %). Furthermore, the overall accuracy of most years exceeded 80 %, the QADI was no higher than 0.2, the user accuracy and producer accuracy were higher than 80 % and 70 %, respectively, for swamps and water bodies, and the remaining non-wetland categories also achieved robust classification accuracy. Thus, the results of this study can compensate for the lack of information on wetland changes in the region over the past 33 years and provide data support and a scientific basis for sustainable wetland development in the plateau.

Abstract Objective To explore the efficacy and sensitivity of 3D gamma analysis and bio-mathematical model for cervical cancer in detecting dose changes caused by dose-calculation-grid-size (DCGS). Methods 17 patients’ plans for cervical cancer were enrolled (Pinnacle TPS, VMAT), and the DCGS was changed from 2.0 mm to 5.0 mm to calculate the planned dose respectively. The dose distribution calculated by DCGS = 2.0 mm as the “reference” data set (RDS), the dose distribution calculated by the rest DCGS as the“measurement”data set (MDS), the 3D gamma passing rates and the (N) TCPs of the all structures under different DCGS were obtained, and then analyze the ability of 3D gamma analysis and (N) TCP model in detecting dose changes and what factors affect this ability. Results The effect of DCGS on planned dose was obvious. When the gamma standard was 1.0 mm, 1.0 and 10.0%, the difference of the results of the DCGS on dose-effect could be detected by 3D gamma analysis (all p value 0.05 accounted for the majority. With DCGS = 2.0 mm being RDS, ∆gamma-passing-rate presented the same trend with ∆(N) TCPs of all structures except for the femurs only when the 1.0 mm, 1.0 and 10.0% standards were adopted for the 3D gamma analysis. Conclusions The 3D gamma analysis and bio-mathematical model can be used to analyze the effect of DCGS on the planned dose. For comparison, the former’s detection ability has a lot to do with the designed standard, and the latter’s capability is related to the parameters and calculated accuracy instrinsically.

IntroductionColorectal cancer (CRC) is the most common gastrointestinal cancer and has a low overall survival rate. Tumor–node–metastasis staging alone is insufficient to predict patient prognosis. Autophagy and long noncoding RNAs play important roles in regulating the biological behavior of CRC. Therefore, establishing an autophagy-related lncRNA (ARlncRNA)-based bioinformatics model is important for predicting survival and facilitating clinical treatment.MethodsCRC data were retrieved from The Cancer Genome Atlas. The database was randomly divided into train set and validation set; then, univariate and multivariate Cox regression analyses were performed to screen prognosis-related ARlncRNAs for prediction model construction. Interactive network and Sankey diagrams of ARlncRNAs and messenger RNAs were plotted. We analyzed the survival rate of high- and low-risk patients and plotted survival curves and determined whether the risk score was an independent predictor of CRC. Receiver operating characteristic curves were used to evaluate model sensitivity and specificity. Then, the expression level of lncRNA was detected by quantitative real-time polymerase chain reaction, and the location of lncRNA was observed by fluorescence in situ hybridization. Additionally, the protein expression was detected by Western blot.ResultsA prognostic prediction model of CRC was built based on nine ARlncRNAs (NKILA, LINC00174, AC008760.1, LINC02041, PCAT6, AC156455.1, LINC01503, LINC00957, and CD27-AS1). The 5-year overall survival rate was significantly lower in the high-risk group than in the low-risk group among train set, validation set, and all patients (all p < 0.001). The model had high sensitivity and accuracy in predicting the 1-year overall survival rate (area under the curve = 0.717). The prediction model risk score was an independent predictor of CRC. LINC00174 and NKILA were expressed in the nucleus and cytoplasm of normal colonic epithelial cell line NCM460 and colorectal cancer cell lines HT29. Additionally, LINC00174 and NKILA were overexpressed in HT29 compared with NCM460. After autophagy activation, LINCC00174 expression was significantly downregulated both in NCM460 and HT29, while NKILA expression was significantly increased.ConclusionThe new ARlncRNA-based model predicts CRC patient prognosis and provides new research ideas regarding potential mechanisms regulating the biological behavior of CRC. ARlncRNAs may play important roles in personalized cancer treatment.

IntroductionAlthough intensity-modulated radiotherapy (IMRT), volumetric-modulated arc therapy (VMAT) and tomotherapy (TOMO) are broadly applied for nasopharyngeal carcinoma (NPC), the best technique remains unclear. Therefore, this study was conducted to address this issue.MethodsThe priority-classified plan optimization model was applied to IMRT, VMAT and TOMO plans in forty NPC patients according to the latest international guidelines. And the dosimetric parameters of planning target volumes (PTVs) and organs at risk (OARs) were compared among these three techniques. The Friedman M test in SPSS software was applied to assess significant differences.ResultsThe median PGTVnx coverage of IMRT was the lowest (93.5%, P < 0.001) for all T categories. VMAT was comparable to TOMO in OARs clarified as priority I and II, and both satisfied the prescribed requirement. IMRT resulted in a relatively high dose for V25 and V30. Interestingly, subgroup analysis showed that the median PTV coverage of the three techniques was no less than 95% in the early T stage. The heterogeneity index (HI) of PGTVnx in VMAT was better than that in IMRT (P = 0.028). Compared to TOMO, VMAT showed a strong ability to protect eyesight and decrease low-dose radiation volumes. In the advanced T stage subgroup, TOMO numerically achieved the highest median PGTVnx coverage volume compared with VMAT and IMRT (93.61%, 91% and 90%, respectively). The best CI and HI of PCTV-1 were observed in TOMO. Furthermore, TOMO was better than VMAT for sparing the brain stem, spinal cord and temporal lobes (all P < 0.05). However, the median V5, V10, V15, V20 and V25 were significantly higher with TOMO than with VMAT (all P < 0.05).ConclusionIn the early T stage, VMAT provides a similar dose coverage and protection of OARs to IMRT, and there are no obvious advantages to choosing TOMO for NPC patients in the early T stage. TOMO may be recommended for patients in the advanced T stage due as it provides the largest dose coverage of PGTVnx and the best protection of the brain stem, spinal cord and temporal lobes. Additionally, more randomized clinical trials are needed for further clarification.

The spatial distribution of night-time lights (NTL) provides a new perspective for studying the range and influence of human activities. However, most studies employing NTL time series are based on monthly or annual composite data, and time series studies incorporating sliding windows are currently lacking. Therefore, using National Polar-Orbiting Partnership’s visible infrared imaging radiometer suite (NPP-VIIRS) night-time light remote sensing (NTLRS) data, VNP46A2, toponym, and Yunnan census statistical data, this study proposes a sliding-window-based NTLRS time series detection and analysis method. We extracted ethnic minority areas on the PyCharm platform using ethnic minority population proportion data and toponym and excluding data representing interference from urban areas. We used a sliding window approach to analyze NTLRS time series data of each ethnic group and calculated the cosine similarity between the NTL brightness curve of original data and the sliding window analysis result. The cosine similarity was greater than 0.96 from 2018 to 2020; we also conducted a field trip to the 2019 Torch Festival to demonstrate the applicability of the employed method. Finally, the temporal and spatial pattern of the Torch Festival was analyzed using the festival in Yunnan Province as an example. Results showed that the Torch Festival, mostly celebrated by the Yi ethnic group, was usually held on the 24th (and ranged from the 22nd to 26th) day in the sixth month of the lunar calendar (LC) every year. We found that during the Torch Festival, the greater the increase in the percentage of NTL brightness reduction in the main urban area of Kunming, the greater the percentage of ethnic minorities’ NTL brightness. The width of the sliding window can be adjusted appropriately according to the research objective, with these results showing good continuity. Our study presents a new application of the sliding window approach in the field of remote sensing, suitable for research into festivals related to night lights and fire all over the world.