Browse Articles
Discover research articles across all indexed journals
Leishmania regulates host YY1: Comparative proteomic analysis identifies infection modulated YY1 dependent proteins
The protein Yin-Yang 1 (YY1) is a ubiquitous multifunctional transcription factor. Interestingly, there are several cellular functions controlled by YY1 that could play a role in Leishmania pathogenesis. Leishmaniasis is a human disease caused by protozoan parasites of the genus Leishmania. This study examined the potential role of macrophage YY1 in promoting Leishmania intracellular survival. Deliberate knockdown of YY1 resulted in attenuated survival of Leishmania in infected macrophages, suggesting a role of YY1 in Leishmania persistence. Biochemical fractionation studies revealed Leishmania infection caused redistribution of YY1 to the cytoplasm from the nucleus where it is primarily located. Inhibition of nuclear transport by leptomycin B attenuates infection-mediated YY1 redistribution and reduces Leishmania survival. This suggests that Leishmania induces the translocation of YY1 from the nucleus to the cytoplasm of infected cells, where it may regulate host molecules to favour parasite survival. A label-free quantitative whole proteome approach showed that the expression of a large number of macrophage proteins was dependent on the YY1 level. Interestingly, several of these proteins were modulated in Leishmania-infected cells, revealing YY1-dependent host response and suggesting their potential role in Leishmania pathogenesis. Together, this study identifies YY1 as a novel virulence factor that promotes Leishmania survival inside host macrophages.
Artificial intelligence for severity triage based on conversations in an emergency department in Korea
A study on the classification and prediction of firefighter’s operational fatigue level
Firefighting operations in high-rise building fires require firefighters to navigate complex environments while undertaking physically demanding, heavy-load tasks, which often lead to severe fatigue, impairing their operational efficiency and decision-making. This study aims to develop a robust fatigue classification and prediction model to assess and forecast firefighters’ fatigue levels. Key metrics, including electrocardiogram (ECG) signals, subjective fatigue ratings, and reaction time data, were utilized. Experiments involving six healthy adult male participants simulated firefighting scenarios, during which subjective fatigue levels (6–20 Borg’s RPE scale) and reaction times were recorded. A five-level fatigue classification was established using K-means clustering, and entropy weight analysis was applied to define a comprehensive fatigue index (F), enabling a three-tier fatigue classification: light, moderate, and severe fatigue. A BP neural network was employed for dynamic fatigue prediction, with 10 features derived from heart rate and heart rate variability (HRV) metrics serving as inputs and the comprehensive fatigue index (F) as the output. The BP neural network model achieved a high prediction accuracy with an R² value of 93.24%, demonstrating its capability to accurately predict firefighters’ fatigue states. This approach provides a scientific basis for optimizing firefighter training protocols and enhancing operational effectiveness during fire rescue missions. The findings highlight the significant potential of this method for advancing firefighter fatigue monitoring and management.
How body knowledge shapes motion perception
How does high temperature weather affect tourists’ nature landscape perception and emotions? A machine learning analysis of Wuyishan City, China
Natural landscapes are crucial resources for enhancing visitor experiences in ecotourism destinations. Previous research indicates that high temperatures may impact tourists’ perception of landscapes and emotions. Still, the potential value of natural landscape perception in regulating tourists’ emotions under high-temperature conditions remains unclear. In this study, we employed machine learning models such as LSTM-CNN, Hrnet, and XGBoost, combined with hotspot analysis and SHAP methods, to compare and reveal the potential impacts of natural landscape elements on tourists’ emotions under different temperature conditions. The results indicate: (1) Emotion prediction and spatial analysis reveal a significant increase in the proportion of negative emotions under high-temperature conditions, reaching 30.1%, with negative emotion hotspots concentrated in the downtown area, whereas, under non-high temperature conditions, negative emotions accounted for 14.1%, with a more uniform spatial distribution. (2) Under non-high temperature conditions, the four most influential factors on tourists’ emotions were Color complexity (0.73), Visual entropy (0.71), Greenness (0.68), and Aquatic rate (0.6). In contrast, under high-temperature conditions, the most influential factors were Greenness (0.6), Openness (0.56), Visual entropy (0.55), and Color complexity (0.55). (3) Compared to non-high temperature conditions, high temperatures enhanced the positive effects of environmental perception on emotions, with Greenness (0.94), Color complexity (0.84), and Enclosure (0.71) showing stable positive impacts. Additionally, aquatic elements under high-temperature conditions had a significant emotional regulation effect (contribution of 1.05), effectively improving the overall visitor experience. This study provides a data foundation for optimizing natural landscapes in ecotourism destinations, integrating the advantages of various machine learning methods, and proposing a framework for data collection, comparison, and evaluation of natural landscape perception under different temperature conditions. It thoroughly explores the potential of natural landscapes to enhance visitor experiences under various temperature conditions and provides sustainable planning recommendations for the sustainable conservation of natural ecosystems and ecotourism.
Relationship between adolescent gaming addiction and myopia, ocular surface condition, and health status: a questionnaire based cohort study
Water ice in the debris disk around HD 181327
Numbers of Eurasian beaver (Castor fiber) in the Czech Republic, causes of their admission to rehabilitation centres and release rates in the period from 2010 to 2020
The Eurasian beaver (Castor fiber) is the largest European rodent. This endangered species is an ecosystem engineer capable of providing several positive impacts in the ecosystems. However, it is also a cause of frequent conflicts with humans. In 2020, the beaver population in the Czech Republic reached 14,610 individuals, with the highest numbers in the Pilsen, Olomouc and Southern Moravian regions. Concurrently, beavers were most often admitted to rehabilitation centres in these regions, and their total numbers in rehabilitation centres increased in the period from 2010 to 2020 (p < 0.01). Beavers were most often admitted after falls into pits and other openings (29.76% of admitted animals) and after a collision with a vehicle (11.9%). Almost half (47.62%) of admitted beavers were released to the wild after their recovery. The mortality rates differed for different causes of admission, with the highest mortality in beavers admitted after a collision with a vehicle (70%) and beavers with bite wounds (67%). There was no significant difference (p > 0.01) in the length of stay in the centres of the beavers that were released to the wild after being treated for the monitored causes of admission. In view of these results, it is important to place particular emphasis on preventive interventions in nature reducing anthropogenic risks for Eurasian beavers and educating the public about the beneficial activities of this endangered species.
Automated high precision PCOS detection through a segment anything model on super resolution ultrasound ovary images
Strategic atom replacement enables regiocontrol in pyrazole alkylation
Serum interferon-gamma-induced protein 10 levels can help predict sarcopenia development in patients with primary hepatocellular carcinoma: A retrospective cohort study
Background Sarcopenia is a prognostic factor in patients with hepatocellular carcinoma (HCC). However, the mechanism underlying sarcopenia development in these patients remains unclear. The chemokine interferon-gamma-induced protein 10/C-X-C motif chemokine ligand 10 (IP-10) has been found to be associated with muscle regeneration or destruction. Thus, we aimed to clarify the role of serum IP-10 levels in predicting sarcopenia development in patients with HCC. Methods This retrospective study enrolled 120 patients with primary HCC whose serum IP-10 levels were measured both at baseline and 1 year after the confirmed diagnosis of HCC. Patients who had sarcopenia at baseline computed tomography imaging were assigned to the Sarco-base group, whereas those in whom sarcopenia was found for the first time after 3 years were assigned to the Sarco-develop group. Those who never met the criteria during the follow-up period were assigned to the Non-Sarco group. Results The baseline IP-10 levels were significantly lower in the Sarco-base group compared to the rest (p = 0.016). Conversely baseline IP-10 levels and IP-10 ratio at 1 year were higher in the Sarco-develop group than in the Non-Sarco group (p = 0.0017, p = 0.025). High IP-10 levels at baseline, and high IP-10 ratios at 1 year were independently related factors for sarcopenia development. Conclusions Patients with sarcopenia at baseline more frequently presented with low IP-10 levels than those without. Contrarily, the group without sarcopenia at baseline and with high baseline IP-10 levels and high IP-10 ratios at 1 year were more likely to develop sarcopenia after 3 years. Monitoring of IP-10 levels may enable the identification of groups prone to develop sarcopenia in patients with HCC.
Temporal interference stimulation over the motor cortex enhances cortical excitability in rats
Quantum error correction of qudits beyond break-even
Scenic area attractiveness in Dali City and its influencing factors evaluated using multi-source spatiotemporal data
Scenic area attractiveness is a core factor in urban tourism development. Developments in social media and multi-source spatiotemporal data provide a basis for studying complex tourist behaviors, overcoming the limitations of traditional interview survey data. This study combines point of interest (POI), mobile signaling, and microblog check-in data to analyze scenic area popularity in Dali using kernel density analysis, hotspot analysis, and gravity models. It also uses ROST-CM6 to perform sentiment analysis on microblog check-in and text data to obtain tourist satisfaction, and combines the popularity and satisfaction to assess scenic area attractiveness. Additionally, GeoDetector is used to examine the impact of subjective human factors, objective factors of the attractions themselves, and the number of POI facilities around the attractions on the scenic area attractiveness in Dali. We obtained several key findings. First, the distribution of scenic areas in Dali City showed a two-center, multi-point pattern, including two core scenic areas (i.e., Dali Ancient City and Xizhou Ancient Town) and numerous scattered areas. Second, the majority of scenic areas in Dali City were more active in the daytime than at night, whereas Dali Ancient City was most active at night. Tourists in Dali City mostly came from Yunnan Province, neighboring provinces, and economically developed coastal regions. Third, a text-based sentiment analysis revealed numerous high-frequency adjectives reflecting positive sentiment, indicating high scenic area satisfaction. Fourth, the number of internal POIs had the greatest effects on scenic area popularity and attractiveness. Specifically, the more POIs, the more popular and attractive the scenic area. The interactive decision-making power of various factors was greater than the decision-making power of individual factors. These findings provide insight into the determinants of scenic area satisfaction, providing a basis for the development of urban tourism.
Evaluation and optimal width ratio selection of microbial mineralization technique in the repair of lining cracks in Xinjiang desert open channel
Emergence of Calabi–Yau manifolds in high-precision black-hole scattering
Abstract When two massive objects (black holes, neutron stars or stars) in our universe fly past each other, their gravitational interactions deflect their trajectories1,2. The gravitational waves emitted in the related bound-orbit system—the binary inspiral—are now routinely detected by gravitational-wave observatories3. Theoretical physics needs to provide high-precision templates to make use of unprecedented sensitivity and precision of the data from upcoming gravitational-wave observatories4. Motivated by this challenge, several analytical and numerical techniques have been developed to approximately solve this gravitational two-body problem. Although numerical relativity is accurate5–7, it is too time-consuming to rapidly produce large numbers of gravitational-wave templates. For this, approximate analytical results are also required8–15. Here we report on a new, highest-precision analytical result for the scattering angle, radiated energy and recoil of a black hole or neutron star scattering encounter at the fifth order in Newton’s gravitational coupling G, assuming a hierarchy in the two masses. This is achieved by modifying state-of-the-art techniques for the scattering of elementary particles in colliders to this classical physics problem in our universe. Our results show that mathematical functions related to Calabi–Yau (CY) manifolds, 2n-dimensional generalizations of tori, appear in the solution to the radiated energy in these scatterings. We anticipate that our analytical results will allow the development of a new generation of gravitational-wave models, for which the transition to the bound-state problem through analytic continuation and strong-field resummation will need to be performed.
Disentangling behavioral determinants of seasonal influenza vaccination in post-corona era: An integrated model approach
Seasonal influenza vaccination (SIV) is influenced by various factors, including sociodemographic characteristics and socioeconomic status of the recipient. Nevertheless, in the post-COVID-19 era, the importance of vaccination and group immunity has grown. Therefore, applying an integrated model to identify behavioral determinants of vaccination is needed. This study aimed to identify contextual factors affecting SIV by applying Andersen’s model. We utilized secondary national datasets (n = 14,535) from the 2022 Community Health Survey conducted by the Korea Disease Control and Prevention Agency. Predisposing factors were gender and age. Enabling factors were income, educational attainment, and marital status. Need factors were presence of chronic disease, health risk behaviors (smoking and/or drinking alcohol), physical activity, and coronavirus disease 2019 (COVID-19) vaccination status. Dependent variable was influenza vaccination status. Multiple binomial logistic regression analyses were performed to identify predictors of influenza vaccination status among Korean adults, stratified by gender and age. According to the results, in men, higher education increased the likelihood of influenza vaccination by 1.089 times (95% CI: 1.000–1.185), while being married increased it by 1.619 times (95% CI: 1.413–1.856); however, smoking and binge drinking reduced the likelihood by 0.822 times (95% CI: 0.732–0.923) and 0.749 times (95% CI: 0.650–0.864), respectively. Among young men, marriage (OR=1.480, 95% CI: 1.131–1.935) and physical activity (OR=1.381, 95% CI: 1.053–1.811) were significant positive factors, while among older men, chronic disease presence increased vaccination likelihood by 1.339 times (95% CI: 1.126–1.592). In women, higher education (OR=1.168, 95% CI: 1.075–1.270) and marriage (OR=2.242, 95% CI: 1.965–2.557) were strong positive predictors, while COVID-19 vaccination history consistently increased influenza vaccination likelihood (OR=1.852, 95% CI: 1.712–2.003). Among young women, smoking reduced vaccination likelihood (OR=0.551, 95% CI: 0.359–0.847), while among older women, having a chronic disease increased vaccination likelihood by 1.354 times (95% CI: 1.133–1.619). This study empirically reveals that SIV is affected by predisposing, enabling, and need factors. To effectively intervene in individual health behaviors, it is necessary to identify characteristics of the population, provide segmented messages, and apply customized strategies.
Time-restricted feeding attenuated hypertension-induced cardiac remodeling by modulating autophagy levels in spontaneously hypertensive rats
The development of the Polish version of the Compassionate Engagement and Action Scales
Compassion has been a subject of extensive scientific research for over two decades. There is clear evidence that our capacity for compassion evolved out of care motivation. Like all motivations it is operated via stimulus response algorithms. For compassion motivation stimulus sensitivity focuses on the processing of indicators of suffering, distress and need, called engagement. The response functions switch attention and processing to what is likely to be helpful in alleviating suffering, distress and need, called action. The Compassion Engagement and Action Scales (CEAS) were developed to measure the S-R algorithm of compassion. Because compassion, like other psychological phenomena can operate interpersonally and intrapersonally, there are three scales that give separate assessments for directing compassion to 1. the self, 2. others and 3. responsiveness to compassion from others. They have been used in many international studies and there is now substantial evidence. The research aimed to validate the CEAS within a Polish population. The three cross-sectional studies involved a total of 1,219 participants from Poland. Confirmatory factor analysis conducted on two separate samples indicates that bifactor models provide the best fit for both the Compassion for Others scale and the Compassion from Others scale. In the first, the model includes a general compassion for others factor alongside specific factors for engagement and actions. Similarly, the second features a general compassion from others factor with the same specific factors. This means that being sensitive to suffering and taking action represent specific components of compassion. However, the bifactor model for Compassion for Self requires further refinement due to lower fit indices and the need for item adjustments. The study results generally support the reliability and validity of the CEAS-PL across diverse samples, aligning with findings from previous studies on the original tool and its language adaptations. Notably, tests of validity—including correlations with emotion regulation, well-being, and attachment styles—highlighted distinct patterns for the three flows of compassion, underscoring their conceptual independence. The CEAS-PL shows promise as a valuable tool for psychological research and practice, especially in the areas of pro-social behaviour and helping people with mental health problems, facilitating the assessment of compassion across different orientations. It may support practitioners in identifying individual competencies and tailoring interventions to enhance compassion-related competencies to address particular difficulties.