In vitro cytotoxicity of biosynthesized titanium dioxide nanoparticles in human prostate cancer cell lines

Purpose: To establish a green method for production of titanium dioxide (TiO2) nanoparticles (NPs) using Cinnamomum tamala (C. tamala) leaf extract, and examine the in vitro cytotoxicity of the product in a human prostate cancer (D145) cell line. Methods: TiO2 NPs were synthesized by mixing 20 mL of C. tamala leaf extract with 0.1 M titanium dioxide (Ti(OH)2) (80 mL) in aqueous solution with stirring for 2 h at room temperature. The TiO2 NPs were characterized using x-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), x-ray photoelectron spectroscopy, dynamic light scattering (DLS), transmission electron microscopy (TEM), selected-area electron diffraction, and energy dispersive x-ray spectroscopy. The in vitro cytotoxicity against D145 cells was determined using a 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide assay. Results: TEM and DLS analyses showed that the NPs were irregularly shaped, with an average particle size of 23 nm. The FT-IR spectrum of C. tamala leaf extract showed that the biomolecules were potentially involved in reduction processes. The negative zeta potential of -14 mV indicated that the NPs were stable and discrete while their crystalline nature was confirmed by XRD. Cytotoxicity analysis showed that the TiO2 NPs exhibit a dose-dependent toxic effect on D145 cells. Conclusion: A facile and less expensive approach for the production of TiO2 NPs using C. tamala leaf extract has been developed. The TiO2 NPs showed dose-dependent cytotoxicity towards D145 cells. Keywords: Anticancer activity, Cinnamomum tamala, Green synthesis, Prostate cancer, TiO2 nanoparticle

A review of high‐velocity impact on fiber‐reinforced textile composites: potential for aero engine applications

Considerable research has indicated that fiber-reinforced textile composites are significantly beneficial to the aerospace industry, especially aero engines, due to their high specific strength, specific stiffness, corrosion resistance, and fatigue resistance. However, damage caused by high-velocity impacts is a critical limitation factor in a wide range of applications. This paper presents an overview of the development, material characterizations, and applications of fiber-reinforced textile composites for aero engines. These textile composites are classified into four categories including two-dimensional (2D) woven composites, 2D braided composites, 3D woven composites, and 3D braided composites. The complex damage mechanisms of these composite materials due to high-velocity impacts are discussed in detail as well

The complete mitochondrial genome of Cryptalaus larvatus (Coleoptera: Elateridae)

In this study, we sequenced the first complete mitochondrial genome of Cryptalaus larvatus. The complete mitochondrial genome of C. larvatus was 15,876 bp with 29.80% GC containing 13 protein-coding genes, 22 transfer RNA (tRNA), two ribosomal RNA (rRNA), as well as and an AT-rich region. Phylogenetic analysis showed that the C. larvatus, Pyrophprus divergens, Ignelater luminosus, Hapsodrilus ignifer, and Dicronychus cinereus were clustered together. This study provides important information for the identification of this species and the study of genetic evolution with other species of Elateridae

Comparative mitochondrial genome analysis of Dendrolimus houi (Lepidoptera: Lasiocampidae) and phylogenetic relationship among Lasiocampidae species.

Dendrolimus houi is one of the most common caterpillars infesting Gymnosperm trees, and widely distributed in several countries in Southeast Asia, and exists soley or coexists with several congeners and some Lasiocampidae species in various forest habitats. However, natural hybrids occasionally occur among some closely related species in the same habitat, and host preference, extreme climate stress, and geographic isolation probably lead to their uncertain taxonomic consensus. The mitochondrial DNA (mtDNA) of D. houi was extracted and sequenced by using high-throughput technology, and the mitogenome composition and characteristics were compared and analyzed of these species, then the phylogenetic relationship was constructed using the maximum likelihood method (ML) and the Bayesian method (BI) based on their 13 protein-coding genes (PCGs) dataset, which were combined and made available to download which were combined and made available to download among global Lasiocampidae species data. Mitogenome of D. houi was 15,373 bp in length, with 37 genes, including 13 PCGs, 22 tRNA genes (tRNAs) and 2 rRNA genes (rRNAs). The positions and sequences of genes were consistent with those of most known Lasiocampidae species. The nucleotide composition was highly A+T biased, accounting for ~80% of the whole mitogenome. All start codons of PCGs belonged to typical start codons ATN except for COI which used CGA, and most stop codons ended with standard TAA or TAG, while COI, COII, ND4 ended with incomplete T. Only tRNASer (AGN) lacked DHU arm, while the remainder formed a typical "clover-shaped" secondary structure. For Lasiocampidae species, their complete mitochondrial genomes ranged from 15,281 to 15,570 bp in length, and all first genes started from trnM in the same direction. And base composition was biased toward A and T. Finally, both two methods (ML and BI) separately revealed that the same phylogenetic relationship of D. spp. as ((((D. punctatus + D. tabulaeformis) + D. spectabilis) + D. superans) + (D. kikuchii of Hunan population + D. houi) as in previous research, but results were different in that D. kikuchii from a Yunnan population was included, indicating that different geographical populations of insects have differentiated. And the phylogenetic relationship among Lasiocampidae species was ((((Dendrolimus) + Kunugia) + Euthrix) + Trabala). This provides a better theoretical basis for Lasiocampidae evolution and classification for future research directions

Editorial IEEE Transactions on Neural Networks and Learning Systems 2016 and Beyond

He H, Chawla N, Chen H, et al. Editorial IEEE Transactions on Neural Networks and Learning Systems 2016 and Beyond. IEEE Transactions on Neural Networks and Learning Systems. 2016;27(1):1-7