Electrochemical treatment of simulated ground water containing MTBE and BTEX with BDD anodes

BACKGROUND: Benzene, toluene, ethylbenzene, xylene (BTEX) and oxy‐fuels such as methyl tert‐butyl ether (MTBE) are typical contaminants of ground waters. Biological and physical techniques are often ineffective in removing these compounds, while promising results were obtained with advanced oxidation processes (AOPs), which are based on oxidation by hydroxyl radicals. Electrochemical oxidation with boron doped diamond (BDD) anodes occurs by OH radicals generated from water oxidation, so that it may be considered as an alternative to other AOPs. RESULTS: An experimental study on the electrochemical removal of MTBE and BTEX with BDD anodes from water with low organic concentrations and low conductivity is presented. The kinetics of the process was investigated by batch electrolyses: the removal of MTBE and benzene was controlled by the mass transfer towards the anode, while a further reaction bulk contribution was found for alkylbenzenes. The process was tested in continuous mode and the energy consumption was evaluated and compared with other AOPs. CONCLUSIONS: Removal of the pollutants higher than 95% was achieved under all the examined conditions, confirming the effectiveness of the process. The proposed electrochemical treatment was comparable with other AOPs in terms of energy consumption, and it can be considered as an alternative to other processes for ground water treatment. Copyright © 2010 Society of Chemical Industry     

Isolation and Characterization of Squamous Cell Carcinoma-Derived Stem-like Cells: Role in Tumor Formation

In human epidermis, keratinocyte stem cells (KSC) are characterized by high levels of β₁-integrin, resulting in the rapid adhesion to type IV collagen. Since epithelial tumors originate from KSC, we evaluated the features of rapidly adhering (RAD) keratinocytes derived from primary human squamous cell carcinoma of the skin (cSCC). RAD cells expressed higher levels of survivin, a KSC marker, as compared to non-rapidly adhering (NRAD) cells. Moreover, RAD cells proliferated to a greater extent and were more efficient in forming colonies than NRAD cells. RAD cells also migrated significantly better than NRAD cells. When seeded in a silicone chamber and grafted onto the back skin of NOD SCID mice, RAD cells formed tumors 2–4 fold bigger than those derived from NRAD cells. In tumors derived from RAD cells, the mitotic index was significantly higher than in those derived from NRAD cells, while Ki-67 and survivin expression were more pronounced in RAD tumors. This study suggests that SCC RAD stem cells play a critical role in the formation and development of epithelial tumors.     

Personalized Medicine in Gastrointestinal Stromal Tumor (GIST): Clinical Implications of the Somatic and Germline DNA Analysis

Gastrointestinal stromal tumors (GIST) are the most common mesenchymal tumors of the gastrointestinal tract. They are characterized by gain of function mutations in KIT or PDGFRA tyrosine kinase receptors, with their consequent constitutive activation. The gold standard therapy is imatinib that offers a good and stable response for approximately 18–36 months. However, resistance is very common and it is vital to identify new biomarkers. Up until now, there have been two main approaches with focus to characterize novel targets. On the one hand, the focus is on the tumor genome, as the final clinical outcome depends mainly from the cancer specific mutations/alterations patterns. However, the germline DNA is important as well, and it is inconceivable to think the patients response to the drug is not related to it. Therefore the aim of this review is to outline the state of the art of the personalized medicine in GIST taking into account both the tumor DNA (somatic) and the patient DNA (germline).     

Extremely low frequency-magnetic fields (ELF-EMF) occupational exposure and natural killer activity in peripheral blood lymphocytes

Extremely Low Frequency-Magnetic Fields (ELF-MF) are possible carcinogens to humans and some data suggest that they can act as promoters or progressors. Since NK cells play a major role in the control of cancer development, an adverse effect on ELF-MF on NK function has been hypothesized. We examined NK activity in 52 workers exposed to different levels of ELF-MF in various activities. Individual exposure was monitored during 3 complete work-shifts using personal dosimeters. Environmental exposure was also monitored. ELF-MF levels in the workers were expressed as Time-Weighted Average (TWA) values. NK activity was measured in peripheral blood lymphocytes (PBL). In the whole group the median occupational TWA was 0.21 microT. According to the TWA levels, workers were classified as low exposed (26 subjects, TWA < or =0.2 microT) and higher exposed workers (26 subjects; TWA >0.2 microT). In higher exposed workers, we observed a trend to reduce NK activity compared to low exposed, but the difference was not significant. Then we selected a subgroup of highest exposed workers (12 subjects; TWA >1 microT); no difference was observed between low and highest exposed subjects in the main personal variables. Considering both E:T ratios from 12:1 to 50:1 and Lytic Units, a significant reduction in NK activity was observed in the highest exposed workers compared to the low exposed. Multivariate analysis showed a significant negative correlation between exposure and LU, while no correlation was evidenced with other personal characteristics. ELF-MF are considered possible carcinogens, and existing data suggest that they can act as promoters. Due to the role of NK activity in host defence against cancer, the results obtained in this study in workers exposed to ELF-MF levels exceeding 1 microT are in agreement with this hypothesis, and support the need for further investigation in this field.     

Incremental template updating for face recognition in home environments

The extreme variability of faces in smart environment applications, due to continuous changes in terms of pose, illumination and subject appearance (hairstyle, make-up, etc.), requires the relevant mode of variations of the subject's faces to be encoded in the templates and to be continuously updated based on new inputs. This work proposes a new video-based template updating approach suitable for home environments where the image acquisition process is totally unconstrained but a large amount of face data is available for continuous learning. A small set of labeled images is initially used to create the templates and the updating is then totally unsupervised. Although the method is here presented in conjunction with a subspace-based face recognition approach, it can be easily adapted to deal with different kinds of face representations. A thorough performance evaluation is carried out to show the efficacy and reliability of the proposed technique.     

Application of proteomic techniques to the study of urine and renal tissue

The increasing application of proteomic methods to biomedical research is providing us with important new information; it holds particular promise in advancing basic and clinical renal research, but whether proteomics can ever become a routine diagnostic tool in nephrology is still uncertain. Currently, proteomic techniques are used by many groups in the search for "biomarkers" of disease, especially kidney disease, because of the ready availability of urine as an "end-product" of renal function. However, the question as to whether any disease-specific biomarkers exist or can be identified by proteomics is also uncertain. A growing application of proteomics in biomedical research is to understand the mechanism(s) of disease. This brief review is selective; in it we consider examples of proteomic studies of human urine for biomarkers, others that have explored renal physiology, and still others that have begun to probe the proteome of organelles. No single approach is sufficiently comprehensive, and the pooled application of proteomics to renal research will undoubtedly improve our understanding of renal function and enable us to explore in more detail subcellular structures, and to characterize cellular processes at the molecular level. When combined with other techniques in renal research, proteomics, and related analytical methods could prove indispensable in modeling renal function, and perhaps also in diagnosis and management of renal disease.     

2D face recognition based on supervised subspace learning from 3D models

One of the main challenges in face recognition is represented by pose and illumination variations that drastically affect the recognition performance, as confirmed by the results of recent face recognition large-scale evaluations. This paper presents a new technique for face recognition, based on the joint use of 3D models and 2D images, specifically conceived to be robust with respect to pose and illumination changes. A 3D model of each user is exploited in the training stage (i.e. enrollment) to generate a large number of 2D images representing virtual views of the face with varying pose and illumination. Such images are then used to learn in a supervised manner a set of subspaces constituting the user's template. Recognition occurs by matching 2D images with the templates and no 3D information (neither images nor face models) is required. The experiments carried out confirm the efficacy of the proposed technique.