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Background The placenta, as a unique exchange organ between mother and fetus, is essential for successful human pregnancy and fetal health. Preeclampsia (PE) caused by placental dysfunction contributes to both maternal and infant morbidity and mortality. Accurate identification of PE patients plays a vital role in the formulation of treatment plans. However, the traditional clinical methods of PE have a high misdiagnosis rate. Results Here, we first designed a computational biology method that used single-cell transcriptome (scRNA-seq) of healthy pregnancy (38 wk) and early-onset PE (28–32 wk) to identify pathological cell subpopulations and predict PE risk. Based on machine learning methods and feature selection techniques, we observed that the Tuning ReliefF (TURF) score hybrid with XGBoost (TURF_XGB) achieved optimal performance, with 92.61% accuracy and 92.46% recall for classifying nine cell subpopulations of healthy placentas. Biological landscapes of placenta heterogeneity could be mapped by the 110 marker genes screened by TURF_XGB, which revealed the superiority of the TURF feature mining. Moreover, we processed the PE dataset with LASSO to obtain 497 biomarkers. Integration analysis of the above two gene sets revealed that dendritic cells were closely associated with early-onset PE, and C1QB and C1QC might drive preeclampsia by mediating inflammation. In addition, an ensemble model-based risk stratification card was developed to classify preeclampsia patients, and its area under the receiver operating characteristic curve (AUC) could reach 0.99. For broader accessibility, we designed an accessible online web server (http://bioinfor.imu.edu.cn/placenta). Conclusion Single-cell transcriptome-based preeclampsia risk assessment using an ensemble machine learning framework is a valuable asset for clinical decision-making. C1QB and C1QC may be involved in the development and progression of early-onset PE by affecting the complement and coagulation cascades pathway that mediate inflammation, which has important implications for better understanding the pathogenesis of PE.

Carbohydrate can be converted into fat by de novo lipogenesis, a process upregulated in fatty liver disease. Chemically, de novo lipogenesis involves polymerization and reduction of acetyl-CoA, using NADPH as the electron donor. The feedstocks used to generate acetyl-CoA and NADPH in lipogenic tissues remain, however, unclear. Here we show using stable isotope tracing in mice that de novo lipogenesis in adipose is supported by glucose and its catabolism via the pentose phosphate pathway to make NADPH. The liver, in contrast, derives acetyl-CoA for lipogenesis from acetate and lactate, and NADPH from folate-mediated serine catabolism. Such NADPH generation involves the cytosolic serine pathway in liver running in the opposite direction to that observed in most tissues and tumours, with NADPH made by the SHMT1-MTHFD1-ALDH1L1 reaction sequence. SHMT inhibition decreases hepatic lipogenesis. Thus, liver folate metabolism is distinctively wired to support cytosolic NADPH production and lipogenesis. More generally, while the same enzymes are involved in fat synthesis in liver and adipose, different substrates are used, opening the door to tissue-specific pharmacological interventions.

This study presents a multi-objective optimization model to solve aircraft landing problem within a predetermined time window. Minimizing total fuel cost, minimizing total delays, maximizing throughput, and maximizing fairness are considered to construct the multi-objective optimization model. According to the different weights of objectives, the first-come-first-serve and simulated annealing are used to solve the multi-objective optimization problem. The algorithms’ feasibility is analysed and a suggestion about how to get satisfactory solutions for all stakeholders is given through the expert evaluation method. The results show that the simulated annealing can get better solution than the first-come-first-serve when the number of flights exceeds 10.

In this paper, the chaotic characteristics of air traffic flow are studied, ADS-B data easily available to ground aviation users are selected as the basic data of traffic flow, and a high-dimensional prediction model of air traffic flow time series based on the non-iterative PSR-ELM algorithm is established. The prediction results of the proposed algorithm are then compared with those of the SVR algorithm, which requires iteration. Moreover, airspace operation data before and after the outbreak of the COVID-19 epidemic are selected as the experimental scene, and the prediction effects of time series with different degrees of chaos are comparatively analyzed. The experimental results reveal that the PSR-ELM algorithm achieves fast and accurate results, and, when the traffic flow state is sparse, the degree of chaos is reduced and the prediction effect is improved. The findings of this research provide a reference for air traffic flow theory.

Per capita fructose consumption has increased 100-fold over the last century1. Epidemiological studies suggest that excessive fructose consumption, and especially consumption of sweet drinks, is associated with hyperlipidaemia, non-alcoholic fatty liver disease, obesity and diabetes2–7. Fructose metabolism begins with its phosphorylation by the enzyme ketohexokinase (KHK), which exists in two alternatively spliced forms8. The more active isozyme, KHK-C, is expressed most strongly in the liver, but also substantially in the small intestine9,10 where it drives dietary fructose absorption and conversion into other metabolites before fructose reaches the liver11–13. It is unclear whether intestinal fructose metabolism prevents or contributes to fructose-induced lipogenesis and liver pathology. Here we show that intestinal fructose catabolism mitigates fructose-induced hepatic lipogenesis. In mice, intestine-specific KHK-C deletion increases dietary fructose transit to the liver and gut microbiota and sensitizes mice to fructose’s hyperlipidaemic effects and hepatic steatosis. In contrast, intestine-specific KHK-C overexpression promotes intestinal fructose clearance and decreases fructose-induced lipogenesis. Thus, intestinal fructose clearance capacity controls the rate at which fructose can be safely ingested. Consistent with this, we show that the same amount of fructose is more strongly lipogenic when drunk than eaten, or when administered as a single gavage, as opposed to multiple doses spread over 45 min. Collectively, these data demonstrate that fructose induces lipogenesis when its dietary intake rate exceeds the intestinal clearance capacity. In the modern context of ready food availability, the resulting fructose spillover drives metabolic syndrome. Slower fructose intake, tailored to intestinal capacity, can mitigate these consequences. Modulating the capacity of the intestinal epithelium to catabolize fructose is shown to alter fructose-induced lipogenesis in the mouse liver, suggesting that fructose clearance in the small intestine protects from steatosis.

Fractal theory is a new scientific method and theory that can describe the complexity and irregularity of nature. Aiming at the problem that the frequency modulation slope obtained by the traditional linear frequency modulation (LFM) signal parameter estimation algorithm has high complexity with poor real-time performance and small SNR adaptation range, the LFM signal frequency modulation slope estimation method based on the fractal-box dimension is proposed. The proposed method is utilized to evaluate the frequency modulation slope of the LFM signal, and the affect of signal amplitude and phase on the fractal-box dimension of the signal is taken into account. The estimation error at different SNRs is analyzed, and the relationship graph of the pulse width, FM bandwidth and fractal-box dimension is drawn. The simulation results demonstrate that the proposed method can accurately estimate the parameters of LFM signals under varying SNR environment. Compared with the traditional linear frequency modulation (LFM) signal parameter estimation algorithms, the anti-noise performance of the proposed algorithm is stronger, and the proposed algorithm is relatively simple and has good application value.

Flight operation data provides a record of air traffic behaviour in detail, and has become an important resource for studying air traffic patterns. In this paper, the degree characteristic of air traffic networks is studied using flight operation data in order to explain air traffic behaviour. The degree indexes were founded from the perspective of network structure and network function based on complex network theory and traffic flow theory. The connectivity degree, traffic degree and delay degree were established, and the characteristics of these three degrees were studied based on American flight data from different days in 2015 over different time periods of each day. Research shows that at all scales the number degrees, volume degrees and duration degrees are all of power-law distribution, and the power exponents are different depending on the scale, date and index. The power exponents fluctuate from 0.5 to 1.5. In general, the connectivity degree exponent is greater than that of the traffic degree and the delay degree. The duration degree exponent is greater than the traffic degree. The exponents are greater on holidays than they are on non-holidays, as well as during non-working hours than during working hours of the day. Compared with non-holidays, the average connectivity degree and delay degree are larger on holidays, and the average traffic degree is smaller. This reveals that most airports only provide flights to one airport, and the airports’ numbers and flight volumes both decrease on holidays. The power indexes of the delay degree and traffic degree are basically the same. The main mode of traffic (arrivals or departures) during each time period is also studied.