A novel risk model based on cuproptosis-related lncRNAs predicted prognosis and indicated immune microenvironment landscape of patients with cutaneous melanoma

Cutaneous melanoma (CM) is an aggressive form of malignancy with poor prognostic value. Cuproptosis is a novel type of cell death regulatory mechanism in tumors. However, the role of cuproptosis-related long noncoding RNAs (lncRNAs) in CM remains elusive. The cuproptosis-related lncRNAs were identified using the Pearson correlation algorithm. Through the univariate and multivariate Cox regression analysis, the prognosis of seven lncRNAs associated with cuproptosis was established and a new risk model was constructed. ESTIMATE, CIBERSORT, and single sample gene set enrichment analyses (ssGSEA) were applied to evaluate the immune microenvironment landscape. The Kaplan–Meier survival analysis revealed that the overall survival (OS) of CM patients in the high-risk group was remarkably lower than that of the low-risk group. The result of the validated cohort and the training cohort indicated that the risk model could produce an accurate prediction of the prognosis of CM. The nomogram result demonstrated that the risk score based on the seven prognostic cuproptosis-related lncRNAs was an independent prognostic indicator feature that distinguished it from other clinical features. The result of the immune microenvironment landscape indicated that the low-risk group showed better immunity than high-risk group. The immunophenoscore (IPS) and immune checkpoints results conveyed a better benefit potential for immunotherapy clinical application in the low-risk groups. The enrichment analysis and the gene set variation analysis (GSVA) were adopted to reveal the role of cuproptosis-related lncRNAs mediated by the immune-related signaling pathways in the development of CM. Altogether, the construction of the risk model based on cuproptosis-related lncRNAs can accurately predict the prognosis of CM and indicate the immune microenvironment of CM, providing a new perspective for the future clinical treatment of CM

Minute-cadence Observations of the LAMOST Fields with the TMTS: III. Statistic Study of the Flare Stars from the First Two Years

Tsinghua University-Ma Huateng Telescopes for Survey (TMTS) aims to detect fast-evolving transients in the Universe, which has led to the discovery of thousands of short-period variables and eclipsing binaries since 2020. In this paper, we present the observed properties of 125 flare stars identified by the TMTS within the first two years, with an attempt to constrain their eruption physics. As expected, most of these flares were recorded in late-type red stars with $G_{\rm BP}-G_{\rm RP}$ > 2.0 mag, however, the flares associated with bluer stars tend to be on average more energetic and have broader profiles. The peak flux (F_peak) of the flare is found to depend strongly on the equivalent duration (ED) of the energy release, i.e., $F_{{\rm peak}} \propto {\rm ED}^{0.72\pm0.04}$, which is consistent with results derived from the Kepler and Evryscope samples. This relation is likely related to the magnetic loop emission, while -- for the more popular non-thermal electron heating model -- a specific time evolution may be required to generate this relation. We notice that flares produced by hotter stars have a flatter $F_{{\rm peak}} \propto {\rm ED}$ relation compared to that from cooler stars. This is related to the statistical discrepancy in light-curve shape of flare events with different colors. In spectra from LAMOST, we find that flare stars have apparently stronger H alpha emission than inactive stars, especially at the low temperature end, suggesting that chromospheric activity plays an important role in producing flares. On the other hand, the subclass having frequent flares are found to show H alpha emission of similar strength in their spectra to that recorded with only a single flare but similar effective temperature, implying that the chromospheric activity may not be the only trigger for eruptions.Comment: 17 pages, 15 figures, 2 tables, refereed version. For associated data files, see https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/523/219

Target Detection over the Diurnal Cycle Using a Multispectral Infrared Sensor

When detecting a target over the diurnal cycle, a conventional infrared thermal sensor might lose the target due to the thermal crossover, which could happen at any time throughout the day when the infrared image contrast between target and background in a scene is indistinguishable due to the temperature variation. In this paper, the benefits of using a multispectral-based infrared sensor over the diurnal cycle have been shown. Firstly, a brief theoretical analysis on how the thermal crossover influences a conventional thermal sensor, within the conditions where the thermal crossover would happen and why the mid-infrared (3~5 μm) multispectral technology is effective, is presented. Furthermore, the effectiveness of this technology is also described and we describe how the prototype design and multispectral technology is employed to help solve the thermal crossover detection problem. Thirdly, several targets are set up outside and imaged in the field experiment over a 24-h period. The experimental results show that the multispectral infrared imaging system can enhance the contrast of the detected images and effectively solve the failure of the conventional infrared sensor during the diurnal cycle, which is of great significance for infrared surveillance applications

Driver Fatigue Detection Using Multitask Cascaded Convolutional Networks

Part 3: Big Data Analysis and Machine LearningInternational audienceDriving fatigue is one of the main reasons of traffic accidents. In this paper, we apply the multitask cascaded convolutional networks to face detection and alignment in order to ensure the accuracy and real-time of the algorithm. Afterwards another convolution neural network (CNN) is used for eye state recognition. Finally, we calculate the percentage of eyelid closure (PERCLOS) to detect the fatigue. The experimental results show that the proposed method has high recognition accuracy of eye state and can detect the fatigue effectively in real- time

NeuralAC: Learning Cooperation and Competition Effects for Match Outcome Prediction

Match outcome prediction in group comparison setting is a challenging but important task. Existing works mainly focus on learning individual effects or mining limited interactions between teammates, which is not sufficient for capturing complex interactions between teammates as well as between opponents. Besides, the importance of interacting with different characters is still largely underexplored. To this end, we propose a novel Neural Attentional Cooperation-competition model (NeuralAC), which incorporates weighted-cooperation effects (i.e., intra-team interactions) and weighted-competition effects (i.e., inter-team interactions) for predicting match outcomes. Specifically, we first project individuals to latent vectors and learn complex interactions through deep neural networks. Then, we design two novel attention-based mechanisms to capture the importance of intra-team and inter-team interactions, which enhance NeuralAC with both accuracy and interpretability. Furthermore, we demonstrate NeuralAC can generalize several previous works. To evaluate the performances of NeuralAC, we conduct extensive experiments on four E-sports datasets. The experimental results clearly verify the effectiveness of NeuralAC compared with several state-of-the-art methods

Development of Sensitive Electrochemical Sensor Based on Chitosan/MWCNTs-AuPtPd Nanocomposites for Detection of Bisphenol A

An electrochemical sensor based on AuPtPd trimetallic nanoparticles functionalized multi-walled carbon nanotubes coupled with chitosan modified glassy carbon electrode (GCE/CS/MWCNTs-AuPtPd) was proposed for the rapid detection of bisphenol A (BPA). AuPtPd trimetallic nanoparticles were first assembled on MWCNTs to obtain MWCNTs-AuPtPd nanocomposite. Then, the MWCNTs-AuPtPd was further dispersed on the chitosan-modified electrode surface to fabricate the GCE/CS/MWCNTs-AuPtPd sensor. Due to the superior catalytic properties of MWCNTs-AuPtPd and the good film formation of chitosan, the constructed sensor displayed good performances for BPA detection. The structural morphology of CS/MWCNTs-AuPtPd was characterized in many ways, such as SEM, TEM and UV-vis. The designed sensor showed a linear relationship in concentration range from 0.05 to 100 µM for BPA detecting, and the detection limit was 1.4 nM. The GCE/CS/MWCNTs-AuPtPd was further used to realize the detection of BPA in food samples, and the recovery was between 94.4% and 103.6%. Those results reflected that the electrochemical sensor designed by CS/MWCNTs-AuPtPd nanocomposites was available, which could be used for the monitoring of food safety

Additional file 1: of Social supports and mental health: a cross-sectional study on the correlation of self-consistency and congruence in China

Social Support Rating Scale (SSRS) (Xiao, 1999). (DOCX 14 kb