Link Prediction via Matrix Completion

Inspired by practical importance of social networks, economic networks, biological networks and so on, studies on large and complex networks have attracted a surge of attentions in the recent years. Link prediction is a fundamental issue to understand the mechanisms by which new links are added to the networks. We introduce the method of robust principal component analysis (robust PCA) into link prediction, and estimate the missing entries of the adjacency matrix. On one hand, our algorithm is based on the sparsity and low rank property of the matrix, on the other hand, it also performs very well when the network is dense. This is because a relatively dense real network is also sparse in comparison to the complete graph. According to extensive experiments on real networks from disparate fields, when the target network is connected and sufficiently dense, whatever it is weighted or unweighted, our method is demonstrated to be very effective and with prediction accuracy being considerably improved comparing with many state-of-the-art algorithms

Transition from Tonks-Girardeau gas to super-Tonks-Girardeau gas as an exact many-body dynamics problem

We investigate transition of a one-dimensional interacting Bose gas from a strongly repulsive regime to a strongly attractive regime, where a stable highly excited state known as the super Tonks-Girardeau gas was experimentally realized very recently. By solving exact dynamics of the integrable Lieb-Liniger Bose gas, we demonstrate that such an excited gas state can be a very stable dynamic state. Furthermore we calculate the breathing mode of the super Tonks-Girardeau gas which is found to be in good agreement with experimental observation. Our results show that the highly excited super Tonks-Girardeau gas phase can be well understood from the fundamental theory of the solvable Bose gas.Comment: 4 pages, 4 figures, version to appear in Phys. Rev. A as a Rapid Communicatio

Preparation of imine alkaloids from norditerpenoids alkaloids

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Risk assessment of dietary exposure of aluminium in residents of Hangzhou

ObjectiveTo evaluate potential health risk and the dietary exposure of aluminium in residents of Hangzhou.MethodsThe dietary exposure of aluminium in residents of Hangzhou was calculated by simple distribution assessment method based on the data of aluminium-containing additives from 2015 to 2021 in Hangzhou and the data of food consumption survey in 2015.ResultsThe average weekly and the 95 percentile （P95） exposure were 0.416 and 1.986 mg/kg·BW， which were lower than the tolerable weekly intake （PTWI， 2 mg/kg·BW） by Joint FAO/WHO Expert Committee on Food Additives （JECFA）. Steamed stuffed buns， noodles， Chinese pastry， fried bread stick and steamed buns were the main sources of exposure， accounting for 85.9% of the total exposure. If the national food safety standard on ammonium-containing additives was strictly implemented， the exposure will reduce by 88.2% and 84.0%， respectively.ConclusionThe risk of dietary aluminium in residents of Hangzhou was low and acceptable. However， the exposure of high-consumption groups （P95） was close to PTWI， and the P95 exposure of people under 18 had exceeded PTWI which deserves attention. Measures should be taken to reduce the health risks caused by excessive aluminium exposure

Temporal change in multimorbidity prevalence, clustering patterns, and the association with mortality: findings from the China Kadoorie Biobank study in Jiangsu Province

Objectives: The characteristics of multimorbidity in the Chinese population are currently unclear. We aimed to determine the temporal change in multimorbidity prevalence, clustering patterns, and the association of multimorbidity with mortality from all causes and four major chronic diseases. Methods: This study analyzed data from the China Kadoorie Biobank study performed in Wuzhong District, Jiangsu Province. A total of 53,269 participants aged 30–79 years were recruited between 2004 and 2008. New diagnoses of 15 chronic diseases and death events were collected during the mean follow-up of 10.9 years. Yule's Q cluster analysis method was used to determine the clustering patterns of multimorbidity. A Cox proportional hazards model was used to estimate the associations of multimorbidity with mortalities. Results: The overall multimorbidity prevalence rate was 21.1% at baseline and 27.7% at the end of follow-up. Multimorbidity increased more rapidly during the follow-up in individuals who had a higher risk at baseline. Three main multimorbidity patterns were identified: (i) cardiometabolic multimorbidity (diabetes, coronary heart disease, stroke, and hypertension), (ii) respiratory multimorbidity (tuberculosis, asthma, and chronic obstructive pulmonary disease), and (iii) mental, kidney and arthritis multimorbidity (neurasthenia, psychiatric disorders, chronic kidney disease, and rheumatoid arthritis). There were 3,433 deaths during the follow-up. The mortality risk increased by 24% with each additional disease [hazard ratio (HR) = 1.24, 95% confidence interval (CI) = 1.20–1.29]. Compared with those without multimorbidity at baseline, both cardiometabolic multimorbidity and respiratory multimorbidity were associated with increased mortality from all causes and four major chronic diseases. Cardiometabolic multimorbidity was additionally associated with mortality from cardiovascular diseases and diabetes, with HRs of 2.64 (95% CI = 2.19–3.19) and 28.19 (95% CI = 14.85–53.51), respectively. Respiratory multimorbidity was associated with respiratory disease mortality, with an HR of 9.76 (95% CI = 6.22–15.31). Conclusion: The prevalence of multimorbidity has increased substantially over the past decade. This study has revealed that cardiometabolic multimorbidity and respiratory multimorbidity have significantly increased mortality rates. These findings indicate the need to consider high-risk populations and to provide local evidence for intervention strategies and health management in economically developed regions

Study on Fabrication and Stability of Starch-Lycium barbarum Complex

In this investigation, the starch-Lycium barbarum complex (CS-LB) was fabricated using corn starch (CS) and Lycium barbarum (LB) through a high-speed shear method. The stability of the guest molecules was also explored. The influence of shear time, rotational speed, and LB to CS mass ratio on Lycium barbarum pigment (LP) content and its stability were investigated. The CS-LB was characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), infrared spectroscopy (FT-IR), and thermogravimetric analysis (TGA). It was found that the content of LP in the product was 0.99±0.03 mg per gram when the shear time was 1.5 hours, the rotational speed was 12000 r/min, and the mass ratio of LB to CS was 3:1. The SEM results illustrated that the products had an agglomerated morphology. The XRD results showed that the crystal domain of starch particles was destroyed and transformed into amorphous structures due to the high-speed shear treatment, but the CS-LP crystalline structure changed into a V-type, which was promoted by the interaction between CS and active components of LB. The FT-IR results showed that the absorption peak at 3421 cm−1 shifted, indicating that CS and LB were bound through hydrogen bonds. The TGA results showed that the thermal stability of the product was also enhanced, with a mass retention rate of 36% at 600 ℃ for the composite. Thus, the CS-LB could be effectively fabricated by high-speed shear treatment. Additionally, it was found that the composite could effectively reduce the effects of temperature, oxygen, and light on the stability of guest molecules in stability experiments. The shelf-life of guest molecules was also extended, enabling them to perform their related functions better