Mineral nutrient imbalance, DNA lesion and DNA-protein crosslink involved in growth retardation of Vicia faba L. seedlings exposed to lanthanum ions

Effects of mineral nutrient imbalance, DNA lesion and DNA-protein crosslink on growth of Vicia faba L. seedlings hydroponically cultivated in concentrations of extraneous lanthanum (La) for 20 days were investigated in the present experiment. The results showed that contents of La, Cu or K elements in roots generally changed synchronously with those in leaves, while Ca, Fe, Zn, Mg, Mn or P in the roots altered inversely to those in the leaves. Thus, the extraneous La led to redistribution and imbalance of mineral nutrient elements in the roots and leaves. DNA lesion and DNA-protein crosslink were investigated by single cell gel electrophoresis (SCGE) and sodium dodecyl sulfate/potassium (SDS/K⁺) precipitation methods, respectively. The results demonstrated that the increasing La induced DNA break and DNA-protein crosslinks (DPCs) in the seedlings. These results suggested that mineral nutrient imbalance, DNA lesion and DNA-protein crosslink were involved in the growth retardation and growth alteration of the seedlings, which may help to understand the mechanisms of rare earth elements (REEs) on plant growth.     

Preparation of birnessite-supported Pt nanoparticles and their application in catalytic oxidation of formaldehyde

Flaky and nanospherical birnessite and birnessite-supported Pt catalysts were successfully prepared and characterized by means of X-ray diffraction (XRD), transmission electron microscopy (TEM), energy dispersive spectroscopy (EDS) and N₂ adsorption-desorption. Effects of the birnessite morphology and Pt reduction method on the catalytic activity for the complete oxidation of formaldehyde (HCHO) were investigated. It was found that flaky birnessite exhibited higher catalytic activity than nanospherical birnessite. The promoting effect of Pt on the birnessite catalyst indicated that the reduction method of the Pt precursor greatly influenced the catalytic performance. Flaky birnessite-supported Pt nanoparticles reduced by KBH₄ showed the highest catalytic activity and could completely oxidize HCHO into CO₂ and H₂O at 50℃, whereas the sample reduced using H₂-plasma showed lower activity for HCHO oxidation. The differences in catalytic activity of these materials were jointly attributed to the effects of pore structure, surface active sites exposed to HCHO and the dispersion of Pt nanoparticles.     

Removal of sulfamethazine antibiotics by aerobic sludge and an isolated Achromobacter sp. S-3

Removal characteristics of sulfamethazine (SMZ) by sludge and a bacterial strain using an aerobic sequence batch reactor (ASBR) were studied. Operating conditions were optimized by varying the reaction time and sludge retention time (SRT). An Achromobacter sp. (S-3) with the ability to remove SMZ was isolated from the ASBR. The effects of different operating parameters (pH and temperature) on the biodegradation of SMZ by S-3 were determined. The results indicate that, between 0.5 and 4 hr, reaction time of the ASBR had a significant effect on the SMZ removal efficiency in the system. The SMZ removal efficiency also increased from 45% to 80% when SRT was prolonged from 5 to 25 days, although longer SRT had no impact on SMZ removal. The SMZ adsorption rate decreased with increasing temperature, which fitted Freundlich isotherm well. The removal of SMZ in the ASBR was due to the combined effects of adsorption and degradation, and degradation played a leading role.     

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

● A novel deep learning framework for short-term water demand forecasting. ● Model prediction accuracy outperforms other traditional deep learning models. ● Wavelet multi-resolution analysis automatically extracts key water demand features. ● An analysis is performed to explain the improved mechanism of the proposed method. Short-term water demand forecasting provides guidance on real-time water allocation in the water supply network, which help water utilities reduce energy cost and avoid potential accidents. Although a variety of methods have been proposed to improve forecast accuracy, it is still difficult for statistical models to learn the periodic patterns due to the chaotic nature of the water demand data with high temporal resolution. To overcome this issue from the perspective of improving data predictability, we proposed a hybrid Wavelet-CNN-LSTM model, that combines time-frequency decomposition characteristics of Wavelet Multi-Resolution Analysis (MRA) and implement it into an advanced deep learning model, CNN-LSTM. Four models - ANN, Conv1D, LSTM, GRUN - are used to compare with Wavelet-CNN-LSTM, and the results show that Wavelet-CNN-LSTM outperforms the other models both in single-step and multi-steps prediction. Besides, further mechanistic analysis revealed that MRA produce significant effect on improving model accuracy.     

Long-term exposure to air pollution and cerebrovascular disease: findings from Beijing Health Management Cohort study

● This study explored the long-term association by double robust additive models. ● Individual exposure concentrations were assessed by integrating GAM, LUR and BPNN. ● PM_2.5, SO₂ and NO₂ are positively associated with cerebrovascular disease. ● CO could reduce the risk of cerebrovascular disease with the highest robustness. ● The elderly, women and people with normal BMI are at higher risk for air pollution. The relationship between air pollution and cerebrovascular disease has become a popular topic, yet research findings are highly heterogeneous. This study aims to investigate this association based on detailed individual health data and a precise evaluation of their exposure levels. The integrated models of generalized additive model, land use regression model and back propagation neural network were used to evaluate the exposure concentrations. And doubly robust additive model was conducted to explore the association between cerebrovascular disease and air pollution after adjusted for demographic characteristics, physical examination, disease information, geographic and socioeconomic status. A total of 25097 subjects were included in the Beijing Health Management Cohort from 2013 to 2018. With a 1 μg/m³ increase in the concentrations of PM_2.5, SO₂ and NO₂, the incidence risk of cerebrovascular disease increased by 1.02 (95% CI: 1.008–1.034), 1.06 (95% CI: 1.034–1.095) and 1.02 (95% CI: 1.010–1.029) respectively. Whereas CO exposure could decrease the risk, with an odds ratio of 0.38 (95% CI: 0.212–0.626). In the subgroup analysis, individuals under the age of 50 with normal BMI were at higher risk caused by PM_2.5, and SO₂ was considered more hazardous to women. Meanwhile, the protective effect of CO on women and those with normal BMI was stronger. Successful reduction of long-term exposure to PM_2.5, SO₂ and NO₂ would lead to substantial benefits for decrease the risk of cerebrovascular disease especially for the health of the susceptible individuals.     

悬浮态水泥生料固硫效率试验研究

在水泥工业领域,水泥生料作为悬浮预热预分解系统脱硫过程中的固硫剂,为了研究固气比、温度、气体流速、SO2浓度以及水泥生料比表面积对悬浮态水泥固硫效率影响规律,采用水泥工厂的生料,利用悬浮电阻炉对水泥生料固硫效率进行试验研究,研究发现固气比和温度对水泥生料固硫效率影响较大。     

铝厂工人尿中1-羟基芘的变化

分别对炼铝厂工人、铝厂附近小学生和对照组人群尿中的1-羟基芘进行了测定.铝厂工人尿中的1-羟基芘为小学生的7.6倍;为对照组人群的43倍.结合多环芳烃的污染情况讨论了用尿中1-羟基芘作为评价铝厂多环芳烃污染的人体接触指标的可行性。     

地下工程中空气离子环境和氡害

近年来国内外大量研究资料表明:环境中空气离子和氡是两个与人舒适感及人体健康密切相关的环境因素。地下工程中空气离子和氡问题比地面室内环境更为突出。随着地下空间广为开发利用,如何改善地下工程中空气离子环境及防氡害问题正在引起人们的极大关注。我们对我国五个地区的37个     

室内小煤炉采暖对尿中1-羟基芘的影响

用尿中1-羟基芘作为指标,观察了室内用小煤炉采暖、暖气采暖和非采暖季节时相同人群尿中1-羟基芘的变化,自身对照和人群对比的测定结果,经数理统计检验后均证明:用小煤炉采暖时人尿中的1-羟基芘显著高于甩暖气采暖或不采暖时的结果,表明用小煤炉采暖造成了室内空气中多环芳烃的严重污染,用尿中1-羟基芘可做为人体接触空气中多环芳烃污染的一个特异、灵敏的指标。     

北京市不同功能区人体接触多环芳烃的生化指标——人尿中1-羟基芘的水平

本文报告北京市不同功能区人尿中1-羟基芘的测定结果,自身对照和群体测定均显示出采暖期与非采暖期的显著差别以及不同功能区样品的差异.尿中1-羟基芘的浓度以焦化厂为最高,为其它各区的4—25倍,依次为工业区、商业区、居民区和对照区,证明用尿中1-羟基芘作为人体接触环境中多环芳烃的一个生物化学指标是可行的.