Summary• Here, we characterized silicon (Si) uptake and xylem loading in Oryza sativa , Zea mays , Helianthus annuus and Benincase hispida in a series of hydroponic experiments. Both active and passive Si-uptake components co-exist in all the plants tested. The active component is the major mechanism responsible for Si uptake in O. sativa and Z. mays .• By contrast, passive uptake prevails in H. annuus and B. hispida at a higher external Si concentration (0.85 m M ), while the active component constantly exists and contributes to the total Si uptake, especially at a lower external Si concentration (0.085 m M ).• Short experiments showed that Si uptake was significantly suppressed in O. sativa and Z. mays by metabolic inhibitors or low temperature, regardless of external Si concentrations. By contrast, Si uptake in H. annuus and B. hispida was inhibited more significantly by metabolic inhibitors or low temperature at lower (for example, 0.085 m M ) than at higher (for example, 1.70 m M ) external Si concentrations.• It can be concluded that both active and passive Si-uptake components co-exist in O. sativa , Z. mays , H. annuus and B. hispida , with their relative contribution being dependent much upon both plant species and external Si concentrations.

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This study aimed at investigating the longitudinal changes of poststroke anxiety/depression rates, and their potential risk factors in acute ischemic stroke (AIS) patients. A total of 250 first diagnosis of AIS patients were enrolled and followed for 36 months. Anxiety/depression of patients were assessed using hospital anxiety and depression scale (HADS) at month (M) 0 (M0) and then every 3 months till M36. During 36-month follow-up, both HADS-anxiety score (from 6.9 ± 3.1 at M0 to 8.0 ± 3.5 at M36) and anxiety rate (from 41.2% at M0 to 54.0% at M36) (both P < .01) were increased with time longitudinally. Meanwhile, HADS-depression score (from 6.2 ± 3.0 at M0 to 6.9 ± 3.1 at M36) and depression rate (from 32.4% at M0 to 40.4% at M36) (both P > .05) displayed an upward trend with time longitudinally but without statistical significance. By forward multivariate logistic regression analysis, female, diabetes and higher National Institute of Health Stroke Scale (NIHSS) score independently predicted elevated anxiety risk at M0, M12, M24, and M36 (all P < .05); while longer education duration and hypertension independently predicted raised anxiety risk at M0 and M12 (all P < .05), respectively. Regarding depression, diabetes independently predicted increased depression risk at M0, M12, M24, and M36 (all P < .01); longer education duration independently predicted higher depression risk at M0 and M12 (both P < .05); female independently predicted increased depression risk at M24 and M36 (both P < .01); higher NIHSS score independently predicted raised depression risk at M24 and M36 (both P < .01). Poststroke anxiety and depression are frequent, which deteriorate with time; besides, female, diabetes, NIHSS score, hypertension and education duration independently predicted increased poststroke anxiety or depression risk in AIS patients.

Six years of hourly aerosol optical thickness (AOT) data retrieved from Himawari-8 were used to investigate the spatial and temporal variations, especially diurnal variations, of aerosols over the China Seas. First, the Himawari-8 AOT data were consistent with the AERONET measurements over most of the China Seas, except for some coastal regions. The spatial feature showed that AOT over high latitude seas was generally larger than over low latitude seas, and it is distributed in strips along the coastline and decreases gradually with increasing distance from the coastline. AOT undergoes diurnal variation as it decreases from 9:00 a.m. local time, reaching a minimum at noon, and then begins to increase in the afternoon. The percentage daily departure of AOT over the East China Seas generally ranged ±20%, increasing sharply in the afternoon; however, over the northern part of the South China Sea, daily departure reached a maximum of >40% at 4:00 p.m. The monthly variation in AOT showed a pronounced annual cycle. Seasonal variations of the spatial pattern showed that the largest AOT was usually observed in spring and varies in other seasons for different seas.