Production of porous aluminium and iron sulphated oxyhydroxides using industrial grade coagulants for optimised arsenic removal from groundwater

The optimisation of Fe and Al oxyhydroxide materials produced using industrial grade coagulants is presented in this work. The effects of synthesis pH and post-synthesis washing procedure onto the arsenic adsorption capacity of the materials were investigated. It was shown that the materials produced at higher pH were more efficient in removing As(V), especially after cleaning procedure. The materials produced at lower pH were less efficient in removing As(V) but the higher presence of sulphate groups in the materials produced at lower pH enhanced As(III) adsorption. Most performing materials can remove up to 84.7 mg As(V) g^-1 or 77.9 mg As(III) g^-1.

DEAD-box helicase DP103 defines metastatic potential of human breast cancers

Despite advancement in breast cancer treatment, 30% of patients with early breast cancers experience relapse with distant metastasis. It is a challenge to identify patients at risk for relapse; therefore, the identification of markers and therapeutic targets for metastatic breast cancers is imperative. Here, we identified DP103 as a biomarker and metastasis-driving oncogene in human breast cancers and determined that DP103 elevates matrix metallopeptidase 9 (MMP9) levels, which are associated with metastasis and invasion through activation of NF-κB. In turn, NF-κB signaling positively activated DP103 expression. Furthermore, DP103 enhanced TGF-β-activated kinase-1 (TAK1) phosphorylation of NF-κB-activating IκB kinase 2 (IKK2), leading to increased NF-κB activity. Reduction of DP103 expression in invasive breast cancer cells reduced phosphorylation of IKK2, abrogated NF-κB-mediated MMP9 expression, and impeded metastasis in a murine xenograft model. In breast cancer patient tissues, elevated levels of DP103 correlated with enhanced MMP9, reduced overall survival, and reduced survival after relapse. Together, these data indicate that a positive DP103/NF-κB feedback loop promotes constitutive NF-κB activation in invasive breast cancers and activation of this pathway is linked to cancer progression and the acquisition of chemotherapy resistance. Furthermore, our results suggest that DP103 has potential as a therapeutic target for breast cancer treatment.

Synthesis of visible-light-activated yellow amorphous TiO2 photocatalyst

Visible-light-activated yellow amorphous TiO₂ (yam- TiO ₂) was synthesised by a simple and organic-free precipitation method. TiN, an alternative precursor for TiO₂ preparation, was dissolved in hydrogen peroxide under acidic condition (pH∼1) adjusted by nitric acid. The yellow precipitate was obtained after adjusting pH of the resultant red brown solution to 2 with NH₄OH. The BET surface area of this sample was 261 m²/g. The visible light photoactivity was evaluated on the basis of the photobleaching of methylene blue (MB) in an aqueous solution by using a 250 W metal halide bulb equipped with UV cutoff filter (λ>420 nm) under aerobic conditions. Yam- TiO₂ exhibits an interesting property of being both surface adsorbent and photoactive under visible light. It was assigned to the η²-peroxide, an active intermediate form of the addition of H₂O₂ into crystallined TiO₂ photocatalyst. It can be concluded that an active intermediate form of titanium peroxo species in photocatalytic process can be synthesised and used as a visible-light-driven photocatalyst

Material Identification of Loose Particles in Sealed Electronic Devices Using PCA and SVM

The existence of loose particles left inside the sealed electronic devices is one of the main factors affecting the reliability of the whole system. It is important to identify the particle material for analyzing their source. The conventional material identification algorithms mainly rely on time, frequency and wavelet domain features. However, these features are usually overlapped and redundant, resulting in unsatisfactory material identification accuracy. The main objective of this paper is to improve the accuracy of material identification. First, the principal component analysis (PCA) is employed to reselect the nine features extracted from time and frequency domains, leading to six less correlated principal components. And then the reselected principal components are used for material identification using a support vector machine (SVM). Finally, the experimental results show that this new method can effectively distinguish the type of materials including wire, aluminum and tin particles.