Social visual preference mediates the effect of cortical thickness on symptom severity in children with autism spectrum disorder

BackgroundEvidence suggests that there is a robust relationship between altered neuroanatomy and autistic symptoms in individuals with autism spectrum disorder (ASD). Social visual preference, which is regulated by specific brain regions, is also related to symptom severity. However, there were a few studies explored the potential relationships among brain structure, symptom severity, and social visual preference.MethodsThe current study investigated relationships among brain structure, social visual preference, and symptom severity in 43 children with ASD and 26 typically developing (TD) children (aged 2–6 years).ResultsSignificant differences were found in social visual preference and cortical morphometry between the two groups. Decreased percentage of fixation time in digital social images (%DSI) was negatively related to not only the thickness of the left fusiform gyrus (FG) and right insula, but also the Calibrated Severity Scores for the Autism Diagnostic Observation Schedule-Social Affect (ADOS-SA-CSS). Mediation analysis showed that %DSI partially mediated the relationship between neuroanatomical alterations (specifically, thickness of the left FG and right insula) and symptom severity.ConclusionThese findings offer initial evidence that atypical neuroanatomical alterations may not only result in direct effects on symptom severity but also lead to indirect effects on symptom severity through social visual preference. This finding enhances our understanding of the multiple neural mechanisms implicated in ASD

Cigarette Flavouring Regulation by Using Aroma-producing Microorganism Isolated from Maotai Daqu

The selected Moutai aromatic microorganisms and their metabolites were applied into the fermentation of tobacco leaves in order to improve the tobacco quality. The results showed that a variety of aromatic substances in Moutai, as well as the typical flavor substances commonly used in cigarettes, were detected in the fermented tobacco leaf extract. In view of the GC-MS results as well as the sensory smoking evaluation of tobacco leaf extracts under designed experimental conditions, the optimal parameters of stable single-strain fermentation process was at 40 ℃ for 10-15 days. The results of specific effects of different fermentation conditions on the content of aroma substances in different parts of tobacco leaves after fermentation, as well as the subsequent sensory evaluation, provided basic data for the improvement of tobacco fermentation and aroma flavoring technology, which was conducive to the development of new cigarettes

Single-cell analysis reveals the COL11A1+ fibroblasts are cancer-specific fibroblasts that promote tumor progression

Background: Cancer-associated fibroblasts (CAFs) promote tumor progression through extracellular matrix (ECM) remodeling and extensive communication with other cells in tumor microenvironment. However, most CAF-targeting strategies failed in clinical trials due to the heterogeneity of CAFs. Hence, we aimed to identify the cluster of tumor-promoting CAFs, elucidate their function and determine their specific membrane markers to ensure precise targeting.Methods: We integrated multiple single-cell RNA sequencing (scRNA-seq) datasets across different tumors and adjacent normal tissues to identify the tumor-promoting CAF cluster. We analyzed the origin of these CAFs by pseudotime analysis, and tried to elucidate the function of these CAFs by gene regulatory network analysis and cell-cell communication analysis. We also performed cell-type deconvolution analysis to examine the association between the proportion of these CAFs and patients’ prognosis in TCGA cancer cohorts, and validated that through IHC staining in clinical tumor tissues. In addition, we analyzed the membrane molecules in different fibroblast clusters, trying to identify the membrane molecules that were specifically expressed on these CAFs.Results: We found that COL11A1+ fibroblasts specifically exist in tumor tissues but not in normal tissues and named them cancer-specific fibroblasts (CSFs). We revealed that these CSFs were transformed from normal fibroblasts. CSFs represented a more activated CAF cluster and may promote tumor progression through the regulation on ECM remodeling and antitumor immune responses. High CSF proportion was associated with poor prognosis in bladder cancer (BCa) and lung adenocarcinoma (LUAD), and IHC staining of COL11A1 confirmed their specific expression in tumor stroma in clinical BCa samples. We also identified that CSFs specifically express the membrane molecules LRRC15, ITGA11, SPHK1 and FAP, which could distinguish CSFs from other fibroblasts.Conclusion: We identified that CSFs is a tumor specific cluster of fibroblasts, which are in active state, may promote tumor progression through the regulation on ECM remodeling and antitumor immune responses. Membrane molecules LRRC15, ITGA11, SPHK1 and FAP could be used as therapeutic targets for CSF-targeting cancer treatment

Mountain Landscape Preferences of Millennials Based on Social Media Data: A Case Study on Western Sichuan

Mountain area is one of the most important modern tourist attractions, and unique mountain landscapes are highly appealing to millennials. Millennials post their travel photos and comments on social media, and these media messages can positively influence other millennials’ travel motivations. To fully understand the attraction of mountain tourist destinations to millennials, this study analyzed their landscape preferences using images posted on social media. As a case study, we analyzed the landscape resources in Western Sichuan Plateau Mountain Areas (WSPMA). We found that differences in genders, modes of transportation, and travel patterns of the millennials influenced their preferences for mountain landscapes. Our results broaden the current knowledge on mountain tourism from the perspective of millennials through social media data. Moreover, studying the landscape resources in WSPMA can facilitate the analysis of regional advantages. This will ultimately enhance tourism publicity and integrate various resources for tourism management and planning in more targeted and attractive ways

Quantitative X-ray fluorescence imaging of gold nanoparticles using joint L1 and total variation regularized reconstruction

This work proposed a joint L1 and total variation (TV) regularized reconstruction method for X-ray fluorescence tomography (XFT), and investigated the performance of this method in quantitative imaging of gold nanoparticles (GNPs). We developed a dual-modality XFT/CT imaging system which consisted of a benchtop X-ray source, a translation/rotation stage, a silicon drift detector for X-ray fluorescence (XRF) detection, and a flat panel detector for transmission X-ray detection. A pencil-beam collimator was 3D printed with steel and employed in sample excitation. The sensitivity of the XFT imaging system was determined by imaging water phantoms with multiple inserts containing GNP solutions of various concentrations (0.02-0.16 wt.%). A joint L1 and total variation (TV) regularized algorithm was developed for XFT reconstruction, where the L1 regularization was used to reduce image artifacts and the TV regularization was used to preserve the shape of targets. Nonlinear conjugate gradient (NCG) descent algorithm with backtracking line search was adopted to solve the reconstruction problem. We compared the L1 + TV regularization method with filtered back projection (FBP), maximum likelihood expectation maximization (ML-EM), L1 regularization, and TV regularization methods. Contrast-to-noise ratio (CNR), Dice similarity coefficient (DSC) and localization error (LE) metrics were used to compare the performance of different methods. The CT and XFT imaging doses were also measured using EBT2 radiochromic films. The 3D printed pencil-beam collimator shaped an excitation beam with a 2 mm full width at half maximum at the imaging isocenter. Based on the phantom imaging experiments, the joint L1 and TV regularization method performed better than FBP, ML-EM, L1 regularization and TV regularization methods, with higher localization accuracy (offset <0.6 mm), CNR and DSC values. Compared with CT, XFT with L1 + TV regularized reconstruction demonstrated higher sensitivity in GNP imaging, and could detect GNP at a concentration of 0.02 wt.% or lower. Moreover, there existed a significant linear correlation (R >0.99) between the reconstructed and true GNP concentration. The estimated XFT imaging dose is about 41.22 cGy under current setting. The joint L1 + TV regularized reconstruction algorithm performed better in noise suppression and shape preservation. Using the L1 + TV regularized reconstruction, the XFT system is able to localize GNP targets with submillimeter accuracy and quantify GNP distribution at a concentration of 0.02 wt.% or lower

Mountain Landscape Preferences of Millennials Based on Social Media Data: A Case Study on Western Sichuan

Mountain area is one of the most important modern tourist attractions, and unique mountain landscapes are highly appealing to millennials. Millennials post their travel photos and comments on social media, and these media messages can positively influence other millennials’ travel motivations. To fully understand the attraction of mountain tourist destinations to millennials, this study analyzed their landscape preferences using images posted on social media. As a case study, we analyzed the landscape resources in Western Sichuan Plateau Mountain Areas (WSPMA). We found that differences in genders, modes of transportation, and travel patterns of the millennials influenced their preferences for mountain landscapes. Our results broaden the current knowledge on mountain tourism from the perspective of millennials through social media data. Moreover, studying the landscape resources in WSPMA can facilitate the analysis of regional advantages. This will ultimately enhance tourism publicity and integrate various resources for tourism management and planning in more targeted and attractive ways

A system for determining maximum tolerated dose in clinical trial

Toxicity study, especially in determining the maximum tolerated dose (MTD) in phase I clinical trial, is an important step in developing new life-saving drugs. In practice, toxicity levels may be categorised as binary grades, multiple grades, or in a more generalised case, continuous grades. In this study, we propose an overall MTD framework that includes all the aforementioned cases for a single toxicity outcome (response). The mechanism of determining MTD involves a function that is predetermined by user. Analytic properties of such a system are investigated and simulation studies are performed for various scenarios. The concept of the continual reassessment method (CRM) is also implied in the framework and Bayesian analysis, including Markov chain Monte Carlo (MCMC) methods are used in estimating the model parameters

Predicting Gasoline Vehicle Fuel Consumption in Energy and Environmental Impact Based on Machine Learning and Multidimensional Big Data

The underestimation of fuel consumption impacts various aspects. In the vehicle market, manufacturers often advertise fuel economy for marketing. In fact, the fuel consumption reference value provided by the manufacturer is quite different from the real-world fuel consumption of the vehicles. The divergence between reference fuel consumption and real-world fuel consumption also has negative effect on the aspects of policy and environment. In order to effectively promote the sustainable development of transport, it is urged to recognize the real-world fuel consumption of vehicles. The gaps in previous studies includes small sample size, single data dimension, and lack of feature weight evaluation. To fill the research gap, in this study, we conduct a comparative analysis through building five regression models to forecast the real-world fuel consumption rate of light-duty gasoline vehicles in China based on big data from the perspectives of vehicle factors, environment factors, and driving behavior factors. Results show that the random forest regression model performs best among the five candidate models, with a mean absolute error of 0.630 L/100 km, a mean absolute percentage error of 7.5%, a mean squared error of 0.805, an R squared of 0.776, and a 10-fold cross-validation score of 0.791. Further, we capture the most important features affecting fuel consumption among the 25 factors from the above three perspectives. According to the relative weight of each factor in the most optimal model, the three most important factors are brake and accelerator habits, engine power, and the fuel economy consciousness of vehicle owners in sequence

Predicting Gasoline Vehicle Fuel Consumption in Energy and Environmental Impact Based on Machine Learning and Multidimensional Big Data

The underestimation of fuel consumption impacts various aspects. In the vehicle market, manufacturers often advertise fuel economy for marketing. In fact, the fuel consumption reference value provided by the manufacturer is quite different from the real-world fuel consumption of the vehicles. The divergence between reference fuel consumption and real-world fuel consumption also has negative effect on the aspects of policy and environment. In order to effectively promote the sustainable development of transport, it is urged to recognize the real-world fuel consumption of vehicles. The gaps in previous studies includes small sample size, single data dimension, and lack of feature weight evaluation. To fill the research gap, in this study, we conduct a comparative analysis through building five regression models to forecast the real-world fuel consumption rate of light-duty gasoline vehicles in China based on big data from the perspectives of vehicle factors, environment factors, and driving behavior factors. Results show that the random forest regression model performs best among the five candidate models, with a mean absolute error of 0.630 L/100 km, a mean absolute percentage error of 7.5%, a mean squared error of 0.805, an R squared of 0.776, and a 10-fold cross-validation score of 0.791. Further, we capture the most important features affecting fuel consumption among the 25 factors from the above three perspectives. According to the relative weight of each factor in the most optimal model, the three most important factors are brake and accelerator habits, engine power, and the fuel economy consciousness of vehicle owners in sequence