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It is not determined whether serum visfatin levels are related to the presence and severity of coronary artery disease (CAD) in non-diabetic subjects. In this study, a total of 65 consecutive non-diabetic participants who underwent coronary angiography were enrolled. Serum visfatin and fasting glucose, as well as the serum total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglyceride, were measured in all participants before the procedure. The extent of coronary artery lesions was determined by Gensini score. Serum visfatin levels were significantly lower in patients with CAD compared to participants with normal coronary arteries. Inversely, the circulating levels of fasting glucose were found to be elevated in patients with CAD compared with the control subjects. Multivariable logistic regression analysis demonstrated that visfatin and impaired fasting glucose (IFG) were independently associated with the presence of CAD in non-diabetics. No significant relationship was found between serum visfatin and fasting glucose levels in IFG subjects. However, there was a negative association between visfatin concentrations and Gensini score in participants with IFG. Both circulating visfatin concentrations and IFG are independently associated with CAD in non-diabetics. Serum visfatin levels are negatively related to the angiographic severity of CAD in subjects with IFG.

Objective To examine the effectiveness of decortication to treat chronic tuberculous empyema (TE) using uniport video-assisted thoracoscopic surgery (VATS) versus conventional triport VATS. Methods Data from consecutive patients with stage II or III TE who underwent decortication with either uniport VATS (uniportal group) between July and December 2017, or triport VATS between January and July 2018 (triportal group), were retrospectively analysed. VATS procedures were performed under general anaesthesia with double lumen endotracheal intubation and clinical outcomes were compared between the two groups. Results Clinical data were comparable between the groups (20 patients in each) regarding demographic and baseline characteristics, operative and postoperative characteristics, surgical procedure-related complications, and postoperative adverse events. No surgical procedure-related complications occurred during the perioperative period in either group. Threshold values for mechanical pain at 8 h postoperatively were significantly higher in the triportal group versus the uniportal group. Furthermore, the incidence of nausea and vomiting was significantly lower in the uniportal versus triportal group. In the triportal group, one patient required readmission and further intervention due to recurrence. Conclusions Uniport VATS decortication for stages II and III TE may be a feasible and safe procedure in selected patients. Moreover, uniport VATS may be less painful than triport VATS.

We present a method of producing bulk cell-cultured fat tissue for food applications. Mass transport limitations (nutrients, oxygen, waste diffusion) of macroscale 3D tissue culture are circumvented by initially culturing murine or porcine adipocytes in 2D, after which bulk fat tissue is produced by mechanically harvesting and aggregating the lipid-filled adipocytes into 3D constructs using alginate or transglutaminase binders. The 3D fat tissues were visually similar to fat tissue harvested from animals, with matching textures based on uniaxial compression tests. The mechanical properties of cultured fat tissues were based on binder choice and concentration, and changes in the fatty acid compositions of cellular triacylglyceride and phospholipids were observed after lipid supplementation (soybean oil) during in vitro culture. This approach of aggregating individual adipocytes into a bulk 3D tissue provides a scalable and versatile strategy to produce cultured fat tissue for food-related applications, thereby addressing a key obstacle in cultivated meat production.

Abstract Amaranthus is a complex taxon with economic importance as well as harmful weeds. We studied the genetic variation and structure of the chloroplast genomes of 22 samples from 17 species of three subgenera. It was found that the length of the chloroplast genome of Amaranthus varied from 149,949 bp of A. polygonoides to 150,757 bp of A. albus. The frequencies of SNPs and InDels in chloroplast genomes were 1.79% and 2.86%, and the variation mainly occurred in the non-coding regions. The longest InDel was 387 bp, which occurred on ycf2, followed by 384 bp InDel on psbM-trnD. Two InDels in ndhE-I on the SSC make the three subgenera clearly distinguished. In LSC, SSC and IRs regions, there were four 30 bp forward and reverse repeats, and the repeats in SSC and LSC were in nearly opposite positions in circular genome structure, and almost divided the circular genome into symmetrical structures. In the topological tree constructed by chloroplast genome, species in subgen. Amaranthus and subgen. Acnida form monophyletic branches separately and cluster together. A. albus, A. blitoides and A. polygonoides were separated from subgen. Albersia, and the rest of subgen. Albersia were clustered into a monophyletic branch. The rpoC2, ycf1, ndhF-rpl32 were good at distinguishing most amaranths. The trnk-UUU-atpF, trnT-UGU-atpB, psbE-clpP, rpl14-rps19, and ndhF-D can distinguish several similar species. In general, the chloroplast genome is of certain value for the identification of the similar species of Amaranthus, which provides more evidence for clarifying the phylogenetic relationships within the genus.

Energy storage technology as a key support technology for China’s new energy development, the demand for critical metal minerals such as lithium, cobalt, and nickel is growing rapidly. However, these minerals have high external dependence and concentrated import sources, increasing the supply risk caused by geopolitics. It is necessary to evaluate the supply risks of critical metal minerals caused by geopolitics to provide a basis for the high-quality development of energy storage technology in China. Based on geopolitical data of eight countries from 2012 to 2020, the evaluation indicators such as geopolitical stability, supply concentration, bilateral institutional relationship, and country risk index were selected to analyze the supply risk of three critical metal minerals, and TOPSIS was applied to construct an evaluation model for the supply risk of critical metal minerals of lithium, cobalt, and nickel in China. The results show that from 2012 to 2017, the security index of cobalt and lithium resources is between .6 and .8, which is in a relatively safe state, while the security index of nickel resources is .2–.4, which is in an unsafe state. From 2017 to 2020, lithium resources remain relatively safe, and the security index of nickel has also risen to between .6 and .7, which is generally in a relatively safe state. However, the security index of cobalt has dropped to .2, which is in an unsafe or extremely unsafe state. Therefore, China needs to pay attention to the safe supply of cobalt resources and formulate relevant strategies to support the large-scale development of energy storage technology.

IntroductionPreeclampsia, one of the leading causes of maternal and fetal morbidity and mortality, demands accurate predictive models for the lack of effective treatment. Predictive models based on machine learning algorithms demonstrate promising potential, while there is a controversial discussion about whether machine learning methods should be recommended preferably, compared to traditional statistical models.MethodsWe employed both logistic regression and six machine learning methods as binary predictive models for a dataset containing 733 women diagnosed with preeclampsia. Participants were grouped by four different pregnancy outcomes. After the imputation of missing values, statistical description and comparison were conducted preliminarily to explore the characteristics of documented 73 variables. Sequentially, correlation analysis and feature selection were performed as preprocessing steps to filter contributing variables for developing models. The models were evaluated by multiple criteria.ResultsWe first figured out that the influential variables screened by preprocessing steps did not overlap with those determined by statistical differences. Secondly, the most accurate imputation method is K-Nearest Neighbor, and the imputation process did not affect the performance of the developed models much. Finally, the performance of models was investigated. The random forest classifier, multi-layer perceptron, and support vector machine demonstrated better discriminative power for prediction evaluated by the area under the receiver operating characteristic curve, while the decision tree classifier, random forest, and logistic regression yielded better calibration ability verified, as by the calibration curve.ConclusionMachine learning algorithms can accomplish prediction modeling and demonstrate superior discrimination, while Logistic Regression can be calibrated well. Statistical analysis and machine learning are two scientific domains sharing similar themes. The predictive abilities of such developed models vary according to the characteristics of datasets, which still need larger sample sizes and more influential predictors to accumulate evidence.

Phytolacca is the largest genus of Phytolaccaceae. Owing to interspecific hybridization, infraspecific variation, and apparent weak genetic control of many qualitative characters, which have obscured boundaries between species, the classification and phylogenetic relationships of this genus are unclear. Native Phytolacca is disjunctly distributed in America, eastern Asia, and Africa, and the biogeographic history of the genus remained unresolved. In this study, we used the whole chloroplast genome and three markers (nrDNA, rbcL, and matK) to reconstruct phylogenetic relationships within Phytolacca, analyze divergence times, and infer biogeographic histories. The phylogenetic results indicate that Phytolacca is monophyletic, which is inconsistent with the infrageneric classification based on morphology. According to the divergence time estimation, Phytolacca began to diversify at approximately 20.30 Ma during the early Miocene. Central America, including Mexico, Costa Rica, and Colombia, is the center of species diversity. Biogeographical analysis indicated five main dispersal events and Phytolacca originated from Central and South America. Birds may be the primary agents of dispersal because of the fleshy fruiting of Phytolacca. This study extended sampling and added more genetic characteristics to infer the evolutionary history of Phytolacca, providing new insights for resolving the classification and elucidating the dispersal events of Phytolacca.

The composites of expanded graphite (EG) and magnetic particles have good electromagnetic wave attenuation properties in the centimeter band, which is valuable in the field of radar wave interference. In this paper, a novel preparation method of Ni–Zn ferrite intercalated EG (NZF/EG) is provided in order to promote the insertion of Ni–Zn ferrite particles (NZF) into the interlayers of EG. The NZF/EG composite is in situ prepared via thermal treatment of Ni–Zn ferrite precursor intercalated graphite (NZFP/GICs) at 900 °C, where NZFP/GICs is obtained through chemical coprecipitation. The morphology and phase characterization demonstrate the successful cation intercalation and NZF generation in the interlayers of EG. Furthermore, the molecular dynamics simulation shows that the magnetic particles in the EG layers tend to disperse on the EG layers rather than aggregate into larger clusters under the synergy of van der Waals forces, repulsive force, and dragging force. The radar wave attenuation mechanism and performance of NZF/EG with different NZF ratios are analyzed and discussed in the range of 2–18 GHz. The NZF/EG with the NZF ratio at 0.5 shows the best radar wave attenuation ability due to the fact that the dielectric property of the graphite layers is well retained while the area of the heterogeneous interface is increased. Therefore, the as-prepared NZF/EG composites have potential application value in attenuating radar centimeter waves.

Large-scale meat production can have negative impacts on public health, the environment and animal welfare. In this Review, the authors consider plant-based and cell-based approaches to meat production and the challenges they face.

Acute ischemic stroke (AIS) is a common neurological disease that seriously endangers both the physical and mental health of human. After AIS, activated immune cells are recruited to the stroke site, where inflammatory mediators are released locally, and severe immune inflammatory reactions occur within a short time, which affects the progress and prognosis of IS. Circular RNA (circRNA) is a type of non-coding RNA (ncRNA) with a closed-loop structure and high stability. Studies have found that circRNA can affect the course of IS. However, there is no report on ceRNA’s pathogenesis in AIS that is mediated by circRNA. In this study, the CIBERSORT algorithm was used to analyze the distribution of immune cells in patients with AIS. mRNA dataset was downloaded from the GEO database, and the weighted gene co-expression network analysis (WGCNA) method was used to construct weighted gene co-expression to determine 668 target genes, using GO, KEGG enrichment analysis, construction of protein-protein interaction (PPI) network analysis, and molecular complex detection (MCODE) plug-in analysis. The results showed that the biological function of the target gene was in line with the activation and immune regulation of neutrophils; signal pathways were mostly enriched in immune inflammation-related pathways. A Venn diagram was used to obtain 52 intersection genes between target genes and disease genes. By analyzing the correlation between the intersection genes and immune cells, we found that the top 5 hub genes were TOM1, STAT3, RAB3D, MDM2, and FOS, which were all significantly positively correlated with neutrophils and significantly negatively correlated with eosinophils. A total of 52 intersection genes and the related circRNA and miRNA were used as input for Cytoscape software to construct a circRNA-mediated ceRNA competition endogenous network, where a total of 18 circRNAs were found. Further analysis of the correlation between circRNA and immune cells found that 4 circRNAs are positively correlated with neutrophils. Therefore, we speculate that there may be a regulatory relationship between circRNA-mediated ceRNA and the immune mechanism in AIS. This study has important guiding significance for the progress, outcome of AIS, and the development of new medicine.

Euphorbia humifusa is a plant species with medicinal and food characteristics used to treat diarrhea and other intestinal diseases. This study investigated the prebiotic effects of E. humifusa-derived polysaccharides (EHPs) on human colonic microbiota and their regulatory effects on ulcerative colitis (UC). Structural characterization showed that EHPs mainly consisted of galactose, glucose, and glucuronic acid and were heteropolysaccharides having molecular weights of 7.70 × 103 and 1.76 × 102 kDa, respectively. EHPs were identified as poorly absorbed macromolecules, verified by the apparent permeability coefficient values (Papp < 1.0 × 10−6 cm/s) and cellular uptake by Caco-2 cell monolayers. During in vitro fermentation studies, the contents of acetic, propionic, and valeric acids increased significantly in EHP-supplemented samples after 24 h compared to that in the control sample. Moreover, EHPs could alter the intestinal microbiota composition by increasing the relative abundance of Bifidobacterium and Holdemanella and reducing that of Escherichia-Shigella, Tyzzerella, and Parasutterella at the genus level. In a dextran sulfate sodium (DSS)-induced UC mouse model, EHPs alleviated UC symptoms by increasing the colon length, reversing the colon tissue damage and inhibiting pro-inflammatory cytokines. Overall, these results suggest that EHPs could be utilized as a potential prebiotic or a promising nutritional strategy for UC management.

It is still an intractable problem to exploit high-efficient Co-based catalysts for low-temperature HCHO oxidation. Herein, we synthesized a series of Cu-doped Co3O4 catalysts (Cu1Co8, Cu1Co4, and Cu1Co2 corresponded to 1/8, 1/4, and 1/2 of Cu/Co molar ratios, respectively) via in situ pyrolysis of bimetal Cu-ZIF-67 precursors and the pure Co3O4 sample was also prepared through directly annealing monometal ZIF-67 for comparison. Performance tests of HCHO oxidation found that Cu doping remarkably enhanced the low-temperature HCHO oxidation performance of Co3O4 sample, and thereinto the Cu1Co4 possessed the optimal HCHO oxidation activity, which achieved 90% HCHO conversion at 108 °C. The characterization results revealed that the stronger interaction between Cu and Co species (Co2+ + Cu2+ ↔ Co3+ + Cu+) of Cu1Co4 not only facilitates the formation of defect sites, Co3+ and surface adsorbed oxygen species but also improves its low-temperature reducibility, and consequently resulting in its superior HCHO oxidation performance. Furthermore, the in-situ DRIFTS results suggested that the formaldehyde oxidation over Cu1Co4 followed HCHO → H2CO2 → HCOO− → CO32− → CO2 pathway. The present work provides a novel and facile approach to fabricating highly effective Co-based catalysts for low-temperature HCHO oxidation.

Previous research has proposed a conceptual framework indicating the mediating role of student engagement on the relationship between effective teaching practices and student academic outcomes. This study is the first internationally to empirically test this conceptual framework for the key aspects of teaching practice—effective learning time and expectations for success—using robust, linked longitudinal survey–administrative data from 6825 public school students in Australia. Drawing on a system of structural equations, we demonstrate that effective learning time has a direct effect on students’ performance and an indirect effect via improved positive behavior, attendance and homework behavior. Expectations for success, in contrast, have a largely indirect effect, affecting students’ academic performance through improved positive behavior, attendance and homework behavior.

The low-carbon transition of the power system is essential for China to achieve peak carbon and carbon neutrality. However, China could suffer power shortages due to radical policies in some extreme cases. The gap between power demand and supply from March 2021 to November 2021 ranged between 5.2 billion kW·h and 24.6 billion kW·h. The main reason for the power shortage was over-reliance on renewable energy and insufficient coal power supply for the power system. The low-carbon transformation path of the electric system needs to be explored with more flexibility for power security. This study applied a modified LEAP model and carried out a forecast analysis of thermal power generation and installed capacity in 2025 and 2030 under normal and extreme weather scenarios. The results suggested that: the installed capacity of thermal power will need to account for about 44.6–46.1% of power generation in 2025 and 37.4–39.3% in 2030, with the assumption of power shortages caused by the instability and uncertainty of renewable power. In the future, China needs to pursue the development of diversified energy sources and enhance the power supply security capability while strengthening the development and utilization of renewable energy.

China has recently put forth an ambitious plan to achieve carbon peak around 2030 and carbon neutrality around 2060. However, there are quite a few differences regarding the public views about China’s carbon policy between the Chinese people and the people from other countries, especially concerning the doubt of foreign people about the fidelity of China’s carbon policy goals. Based on Twitter data related to China’s carbon policy topics from 2008 to 2020, this study shows the inter- and intra-annual trends in the count of tweets about China’s carbon policy, conducts sentiment analysis, extracts top frequency words from different attitudes, and analyzes the impact of China’s official Twitter accounts on the global view of China’s carbon policy. Our results show: (1) the global attention to China’s carbon policy gradually rises and occasionally rises suddenly due to important carbon events; (2) the proportion of Twitter users with negative sentiment about China’s carbon policy has increased rapidly and has exceeded the proportion of Twitter users with positive sentiment since 2019; (3) people in developing countries hold more positive or neutral attitudes towards China’s carbon policy, while developed countries hold more negative attitudes; (4) China’s official Twitter accounts serve to improve the global views on China’s carbon policy.

Understanding the drivers of student dropout from higher education has been a policy concern for several decades. However, the contributing role of certain factors--including student mental health--remains poorly understood. Furthermore, existing studies linking student mental health and university dropout are limited in both methodology and scope--for example, they often rely on small and/or non-representative samples or subjective measures, and focus almost exclusively on main effects. This paper overcomes many of these shortcomings by leveraging unique linked administrative data on the full population of domestic students commencing undergraduate studies at Australian universities between 2012 and 2015 (n = 652,139). Using these data, we document that approximately 15% of students drop out of university within their first academic year. Critically, students receiving treatment for mental health problems are 4.3 (adjusted) to 8.3 (unadjusted) percentage points more likely to drop out of higher education. This association remains in the presence of an encompassing set of potential confounds, and is remarkably uniform across segments of the student population determined by individual, family, and programme characteristics. Altogether, our findings call for increased policy efforts to improve student mental health and to buffer against its deleterious effects on retention.

Driven by the focus on standardised assessment and performance-driven accountability, a considerable body of literature has documented differences in students' academic achievement across school sectors, both internationally and in Australia. However, few studies have to date explored the potential mechanisms underlying such differences, particularly through the lens of school climate and student engagement. And despite extensive literature on school climate and student engagement, including their relationships with achievement, the differences in these patterns across school sectors remain under-studied. In this paper, we leverage nationally representative data from a large-scale longitudinal survey in Australia with linked administrative data on student achievement to reveal different patterns of school climate and student engagement across government, Catholic and independent sectors. Employing multivariable regression analyses, we identify unique school climate and student engagement facets that are associated with improved achievement in each of these sectors, offering important pointers for educational policies.

This work applies two levels of inference within a Bayesian framework to accomplish estimation of the directions of arrivals (DoAs) of sound sources. The sensing modality is a spherical microphone array based on spherical harmonics beamforming. When estimating the DoA, the acoustic signals may potentially contain one or multiple simultaneous sources. Using two levels of Bayesian inference, this work begins by estimating the correct number of sources via the higher level of inference, Bayesian model selection. It is followed by estimating the directional information of each source via the lower level of inference, Bayesian parameter estimation. This work formulates signal models using spherical harmonic beamforming that encodes the prior information on the sensor arrays in the form of analytical models with an unknown number of sound sources, and their locations. Available information on differences between the model and the sound signals as well as prior information on directions of arrivals are incorporated based on the principle of the maximum entropy. Two and three simultaneous sound sources have been experimentally tested without prior information on the number of sources. Bayesian inference provides unambiguous estimation on correct numbers of sources followed by the DoA estimations for each individual sound sources. This paper presents the Bayesian formulation, and analysis results to demonstrate the potential usefulness of the model-based Bayesian inference for complex acoustic environments with potentially multiple simultaneous sources.

Analysis of knee joint vibration or vibroarthrographic (VAG) signals using signal processing and machine learning algorithms possesses high potential for the noninvasive detection of articular cartilage degeneration, which may reduce unnecessary exploratory surgery. Feature representation of knee joint VAG signals helps characterize the pathological condition of degenerative articular cartilages in the knee. This paper used the kernel-based probability density estimation method to model the distributions of the VAG signals recorded from healthy subjects and patients with knee joint disorders. The estimated densities of the VAG signals showed explicit distributions of the normal and abnormal signal groups, along with the corresponding contours in the bivariate feature space. The signal classifications were performed by using the Fisher’s linear discriminant analysis, support vector machine with polynomial kernels, and the maximal posterior probability decision criterion. The maximal posterior probability decision criterion was able to provide the total classification accuracy of 86.67% and the area (Az) of 0.9096 under the receiver operating characteristics curve, which were superior to the results obtained by either the Fisher’s linear discriminant analysis (accuracy: 81.33%, Az: 0.8564) or the support vector machine with polynomial kernels (accuracy: 81.33%, Az: 0.8533). Such results demonstrated the merits of the bivariate feature distribution estimation and the superiority of the maximal posterior probability decision criterion for analysis of knee joint VAG signals.

Analysis of gait dynamics in children may help understand the development of neuromuscular control and maturation of locomotor function. This paper applied the nonparametric Parzen-window estimation method to establish the probability density function (PDF) models for the stride interval time series of 50 children (25 boys and 25 girls). Four statistical parameters, in terms of averaged stride interval (ASI), variation of stride interval (VSI), PDF skewness (SK), and PDF kurtosis (KU), were computed with the Parzen-window PDFs to study the maturation of stride interval in children. By analyzing the results of the children in three age groups (aged 3–5 years, 6–8 years, and 10–14 years), we summarize the key findings of the present study as follows. (1) The gait cycle duration, in terms of ASI, increases until 14 years of age. On the other hand, the gait variability, in terms of VSI, decreases rapidly until 8 years of age, and then continues to decrease at a slower rate. (2) The SK values of both the histograms and Parzen-window PDFs for all of the three age groups are positive, which indicates an imbalance in the stride interval distribution within an age group. However, such an imbalance would be meliorated when the children grow up. (3) The KU values of both the histograms and Parzen-window PDFs decrease with the body growth in children, which suggests that the musculoskeletal growth enables the children to modulate a gait cadence with ease. (4) The SK and KU results also demonstrate the superiority of the Parzen-window PDF estimation method to the Gaussian distribution modeling, for the study of gait maturation in children.

Coevolutionary arms races between predators and prey can lead to a diverse range of foraging and defense strategies, such as countermeasures between nocturnal insects and echolocating bats. Here, we show how the fine structure of wing scales may help moths by slightly increasing sound absorbance at frequencies typically used in bat echolocation. Using four widespread species of moths and butterflies, we found that moth scales are composed of honeycomb-like hollows similar to sound-absorbing material, but these were absent from butterfly scales. Micro-reverberation chamber experiments revealed that moth wings were more absorbent at the frequencies emitted by many echolocating bats (40-60 kHz) than butterfly wings. Furthermore, moth wings lost absorbance at these frequencies when scales were removed, which suggests that some moths have evolved stealth tactics to reduce their conspicuousness to echolocating bats. Although the benefits to moths are relatively small in terms of reducing their target strengths, scales may nonetheless confer survival advantages by reducing the detection distances of moths by bats by 5-6%.

The semantic segmentation network based on U-Net (U-shaped network structure) in Si3N4 ceramic bearing cylindrical roller surface defect segmentation has the problems that low accuracy of defect feature extraction and large number of model parameters. It cannot meet the segmentation accuracy requirements of different size defects. The semantic segmentation network (D-A-IU-Net-Semantic segmentation network model with embedded multi-scale defective features) with embedded multi-scale defective feature fusion module is proposed. D-A-IU-Net inherits the encoding–decoding features of U-Net semantic segmentation network in structure and has the ability to sense multi-layer defective features. To reduce the number of model parameters and the network model training time, the D-A-IU-Net network model is downsampled only three times. To address the loss of detailed semantic information during downsampling, the network adopts a depthwise separable convolution layer with stride = 2 instead of the maximum pooling layer in the coding module. The segmentation accuracy is further improved. For overcoming the low segmentation accuracy of tiny defects on Si3N4 ceramic bearing cylindrical roller surface, the Atrous Spatial Pyramid Pooling Module (ASSP module-Hollow space convolution pool pyramid) is added to the bottom of the D-A-IU-Net network model to replace the model structure at the bottom of the U-net network model. To avoid the performance degradation caused by the increasing depth of the network model, two identity blocks are added at the bottom. A dataset of Si3N4 ceramic bearing cylindrical roller surface defects is prepared for validation. The experimental results show that the mIoU (mean Intersection over Union) of the D-A-IU-Net network model reaches 90.70%, the mPA (mean Pixel Accuracy) is improved to 95.13%, and the single image prediction time is 46.53 ms. The region segmentation efficiency of Si3N4 ceramic bearing cylindrical roller surface defects is improved. [ABSTRACT FROM AUTHOR]