Pb(II) and chlortetracycline immobilization and economy of biologically amended coastal soil.

To study the pollutants immobilization and economy of biologically amended coastal soil, Alternanthera philoxeroides biomass (Bm), biochar (Bc), and dodecyldimethyl betaine (BS) modified Bc (BS-Bc) were used to amend coastal soil from Jialing, Fu, and Qu River. A runoff experiment was used to simulate the longitudinal migration and morphological changes of Pb(II) and chlortetracycline (CTC) in each amended coastal soil, and the economy of pollutants immobilization by different amended coastal soil were compared. The equilibrium time of Pb(II) and CTC in each amended coastal soil ranked in the order of BS-Bc-amended > Bc-amended > Bm-amended > unamended coastal soil. The average Pb(II) and CTC flow rate in different amended coastal soils presented an opposite trend with the equilibrium time. Pb(II) and CTC content all reduced with the increasing runoff length. Under the same soils, the content changes presented Bm and Bc amended > unamended > BS-Bc amended. CEC and clay content of coastal soils were the key factors affecting Pb(II) and CTC immobilization. The immobilization mechanisms were electrostatic attraction, ion exchange, surface precipitation, and complexation to Pb(II) and ion exchange and complexation to CTC. The economy of Pb(II) and CTC immobilization ranged from 0.5 to 9.0 and from 1.0 to 5.4 mg/¥, and coastal soil amended by BS-Bc had practical application value and high economy.

Evaluations on supervised learning methods in the calibration of seven-hole pressure probes.

Machine learning method has become a popular, convenient and efficient computing tool applied to many industries at present. Multi-hole pressure probe is an important technique widely used in flow vector measurement. It is a new attempt to integrate machine learning method into multi-hole probe measurement. In this work, six typical supervised learning methods in scikit-learn library are selected for parameter adjustment at first. Based on the optimal parameters, a comprehensive evaluation is conducted from four aspects: prediction accuracy, prediction efficiency, feature sensitivity and robustness on the failure of some hole port. As results, random forests and K-nearest neighbors' algorithms have the better comprehensive prediction performance. Compared with the in-house traditional algorithm, the machine learning algorithms have the great advantages in the computational efficiency and the convenience of writing code. Multi-layer perceptron and support vector machines are the most time-consuming algorithms among the six algorithms. The prediction accuracy of all the algorithms is very sensitive to the features. Using the features based on the physical knowledge can obtain a high accuracy predicted results. Finally, KNN algorithm is successfully applied to field measurements on the angle of attack of a wind turbine blades. These findings provided a new reference for the application of machine learning method in multi-hole probe calibration and measurement.

Linking the low-density lipoprotein receptor-binding segment enables the therapeutic 5-YHEDA peptide to cross the blood-brain barrier and scavenge excess iron and radicals in the brain of senescent mice.

INTRODUCTION: Iron accumulates in the brain during aging, which catalyzes radical formation, causing neuronal impairment, and is thus considered a pathogenic factor in Alzheimer's disease (AD). To scavenge excess iron-catalyzed radicals and thereby protect the brain and decrease the incidence of AD, we synthesized a soluble pro-iron 5-YHEDA peptide. However, the blood-brain barrier (BBB) blocks large drug molecules from entering the brain and thus strongly reduces their therapeutic effects. However, alternative receptor- or transporter-mediated approaches are possible. METHODS: A low-density lipoprotein receptor (LDLR)-binding segment of Apolipoprotein B-100 was linked to the 5-YHEDA peptide (bs-5-YHEDA) and intracardially injected into senescent (SN) mice that displayed symptoms of cognitive impairment similar to those of people with AD. RESULTS: We successfully delivered 5-YHEDA across the BBB into the brains of the SN mice via vascular epithelium LDLR-mediated endocytosis. The data showed that excess brain iron and radical-induced neuronal necrosis were reduced after the bs-5-YHEDA treatment, together with cognitive amelioration in the SN mouse, and that the senescence-associated ferritin and transferrin increase, anemia and inflammation reversed without kidney or liver injury. DISCUSSION: bs-5-YHEDA may be a mild and safe iron remover that can cross the BBB and enter the brain to relieve excessive iron- and radical-induced cognitive disorders.

Current perspectives of SA-4-1BBL in immune modulation during cancer.

A recombinant co-stimulatory molecule capable of inducing multiple effects on varied immune cells when present in its soluble active form is termed as SA-4-1BBL. It has been reported to influence innate, adaptive, and regulatory immune cells. Recent studies confirmed its engagement with receptor, 4-1BB leading to collection of interleukin-2 (IL-2) that in turn overcomes Treg suppression. Further, a vast number of pre-clinical studies reported its therapeutic efficacy in the form of adjuvant subunit in cancer vaccines. Furthermore, it is also observed that it contributes significantly towards communication bridge of CD4 and NK cells. On the other hand, depletion of either NK or CD4 cells negated SA-4-1BBL's antitumor protection. The present review article is focused on the current updates of this molecule pertaining to the filed of cancer therapeutics or cancer preventives.

Clinicopathological significance of WIF1 hypermethylation in NSCLC, a meta-analysis and literature review.

Methylation of the WIF-1 gene can lead to the loss of WIF-1 expression which has been observed in numerous types of cancer including NSCLC. However, the association and clinicopathological significance between WIF-1 promoter hypermethylation and NSCLC remains unclear. In the present study, we performed a meta-analysis to evaluate the clinicopathological significance of WIF-1 hypermethylation in NSCLC. A systematic literature search was carried out using Pubmed, EMBASE, Web of Science and CNKI. The Cochrane software Review manager 5.2 was used. The frequency of WIF-1 hypermethylation was significantly increased in NSCLC compared with normal lung tissue; the pooled OR was 8.67 with 95% CI 1.64-45.88, p = 0.01. The rate of WIF-1 hypermethylation was higher in SCC than in AC, OR was 1.74 with 95% CI 0.97-3.11, p = 0.06. In addition, WIF-1 loss was correlated with low 5-year survival rate. In summary, WIF-1 hypermethylation is a potential biomarker for diagnosis of NSCLC. WIF-1 hypermethylation is predominant in squamous cell carcinoma (SCC), suggesting that WIF-1methylation contributes to the development of NSCLC, especially SCC.