Effects of Kevlar® 29 yarn twist on tensile and tribological properties of self-lubricating fabric liner

Yarn twist in textile technology is an important characteristic since it considerably affects the properties of knitted or woven fabrics. Many researchers have investigated the effect of staple-spun yarn twist on the properties of the yarns and fabrics. However, the effects of twist level of Kevlar® 29 filament yarn on the properties of yarn and its resin-impregnated self-lubricating fabric liner are not fully known yet. In this study, we have investigated the effects of Kevlar® 29 twist level on the tensile and tribological properties of the fabric liner (Kevlar® 29/polytetrafluoroethylene fabric-resin composite). Two unexpected findings about the effect of yarn twist have been observed, namely (1) asynchronous twist effect on the yarn’s and the liner’s tensile strength and (2) dissimilar yarn twist effect on the liner’s performance. These findings are mainly attributed to the synergic contributions of the yarn twist and strength and the interaction of the resin with the yarn orientation in the woven fabric structure of the liner

Alignment method of internal and external pipeline inspection data based on machine learning algorithm

As an important part of pipeline integrity management, the alignment of the external and internal pipeline inspection data is to align the external inspection points to the internal inspection centerline, so as to fully mine the value of internal and external inspection data. Herein, from the perspective of the relationship between the surface marking points and the mileage piles of the pipelines, a relation model of internal inspection points and external inspection mileages was constructed using the machine learning algorithm based on the external and internal inspection data from the pipeline integrity management system of a long-distance pipeline company, and the mileage information of the internal inspection points in the external inspection was predicted to increase the mapping between the internal inspection points and the external inspection mileage, further realizing the data enhancement. In addition, the pipelines were segmented by the surface marking points and mileage piles, and the external inspection points were aligned to the internal inspection centerline with the linear stretching algorithm, so as to realize the alignment of the external and internal inspection data. The results show that the average absolute percent error is less than 0.10% and the determination coefficient is 99.99% for the internal inspection point based external inspection mileage prediction model established with the machine learning algorithm. Moreover, the model could capture the relationship between the internal inspection points and the external inspection mileages, so as to support the automatic alignment of the external and internal inspection data of the pipelines

A Flexible and Divergent Strategy to Flavonoids with a Chiral A‑Ring Featuring Intramolecular Michael Addition: Stereoselective Synthesis of (+)-Cryptocaryone, (+)-Cryptogione F, and (+)-Cryptocaryanones A and B, as Well as (+)-Cryptochinones A and C

A flexible strategy has been developed to synthesize divergent flavonoids bearing a chiral A-ring. As two key steps, the coupling via a boron-mediated aldol condensation and the cyclization via a highly stereoselective intramolecular Michael addition of 1,3-diketone proceed under mild conditions; thus, the chiral flavonoids bearing C-7 oxy functional groups or olefinic bonds are both easily accessible. Using this approach, the first synthesis of (+)-cryptogione F, (+)-cryptocaryanone B, and (+)-cryptochinones A and C, as well as stereoselective synthesis of (+)-cryptocaryone and (+)-cryptocaryanone A, were achieved from 2-deoxy-d-ribose in high overall yields

A Stereoselective Approach toward (−)-Lepadins A–C

A new short approach to (−)-lepadins A–C has been developed based on a stereocontrolled Diels–Alder reaction employing a chiral dienophile. With this approach, (−)-lepadin B is synthesized from 5-deoxy-d-ribose in 13 steps with 14.8% overall yield. The <i>cis</i>-decahydro­quinoline core containing five stereocenters could be rapidly constructed via stereoselective cycloaddition and subsequent five-step one-pot hydrogenation–cyclization

Correction: Inhibition of Nickel Nanoparticles-Induced Toxicity by Epigallocatechin-3-Gallate in JB6 Cells May Be through Down-Regulation of the MAPK Signaling Pathways.

[This corrects the article DOI: 10.1371/journal.pone.0150954.]

Joint Toxicity of Different Heavy Metal Mixtures after a Short-Term Oral Repeated-Administration in Rats

The systemic toxicity of different combinations of heavy metal mixtures (HMMs) was studied according to equivalent proportions of the eight most common detectable heavy metals found in fish consumption in the Ningbo area of China. The ion mass proportions of Zn, Cu, Mn, Cr, Ni, Cd, Pb, and Hg were 1070.0, 312.6, 173.1, 82.6, 30.0, 13.3, 6.6, and 1.0, respectively. In this study, 10 experimental groups were set as follows: M8 (Pb + Cd + Hg + Ni + Cu + Zn + Mn + Cr); M5 (Pb + Cd + Hg + Ni + Cr); M4A (Pb + Cd + Hg + Ni); M4B (Cu + Zn + Mn + Cr); M3 (Cu + Zn + Mn); Cr; Cu; Zn; Mn; and control. Sprague Dawley (SD) rats were orally treated with a single dose of each group every three days (10 times in total) for 34 days. After Morris water maze test, blood and tissue samples were collected to obtain biochemical, histopathological and western blot analysis. Results show abnormalities could be observed in different treatment groups, the M4B combination had the most significant change compared to all other groups. In conclusion, combination HMMs may have adverse effects on the hematologic, hepatic, renal and neurobehavioral function, and may also disturb electrolyte and lipid balance. Why M4B combination generated much higher toxic effects than any other combination mixtures or individual heavy metal needs to be further evaluated

Luciferase activity of AP-1 and NF-κB after JB6 cells were treated with Ni NPs alone or Ni NPs + EGCG.

<p>Notes: *<i>P</i><0.05, Ni NPs alone compared with Ni NPs + EGCG; + <i>P</i><0.05, ++ <i>P</i><0.01, +++ <i>P</i><0.001, compared with control—(without any treatment); # <i>P</i><0.01, ## <i>P</i><0.01, ### <i>P</i><0.001, compared with control + 10 μM EGCG. 20 nM TPA was set as a positive control. Abbreviations: Ni NPs, nickel nanoparticles; EGCG, epigallocatechin-3-gallate; TPA, phorbol ester.</p