Electrochemical corrosion behavior of LDX 2101® duplex stainless steel in a fluoride-containing environment

The effect of fluoride on the electrochemical corrosion behavior of an LDX 2101® duplex stainless steel (DSS) was studied. Open-circuit potential (E_OC) and electrochemical impedance spectroscopy (EIS) measurements were carried out in artificial saliva and with the addition of fluoride (1 wt% NaF). The electrochemical corrosion behavior of the AISI 316L austenitic stainless steel (SS) was also evaluated for comparison. Both open-circuit potential and EIS results indicate that DSS and austenitic SS undergo spontaneous passivation due to spontaneously formed oxide film passivating the metallic surface, in the simulated aggressive environments. However, LDX 2101® exhibits superior corrosion resistance as compared with AISI 316L, and this improvement is ascribed to the formation of a passive film which shows a higher protective effect than the one formed on AISI 316L.     

Autochthonous white grape pomaces as bioactive source for functional jams

Seeds and skins from grape pomaces of Pecorello and Mantonico cv underwent extraction (ultrasound–assisted or maceration), in order to obtain added-value ingredients for the production of a functional pear jam. The antioxidant features of the extracts were tested by in vitro colorimetric assays. Among seeds, Mantonico by maceration (MSC) showed the best results, as well as Mantonico by ultrasound-assisted extraction (MBs) among skin extracts. The selected extracts were fully characterised by NMR and MS techniques, confirming the presence of many polyphenols, flavonoids and tannins among others. Pectin was then derivatised by the grafting procedure with the active molecules of MBs and MSC. The latter produced the best antioxidant polymer also without toxicity evaluated using Caco-2 cells and was used for the jam preparation. The functional pear jam showed improved antioxidant performances in comparison with its non-functional counterparts as well as its maintenance over time (15 days).     

Reduced graphene oxide-ZnO hybrid composites as photocatalysts: The role of nature of the molecular target in catalytic performance

Spurred by controversial literature findings, we enwrapped reduced graphene oxide (rGO) in ZnO hierarchical microstructures (rGO loadings spanning from 0.01 to 2 wt%) using an in situ synthetic procedure. The obtained hybrid composites were carefully characterized, aiming at shining light on the possible role of rGO on the claimed increased performance as photocatalysts. Several characterization tools were exploited to unveil the effect exerted by rGO, including steady state and time resolved photoluminescence, electron microscopies and electrochemical techniques, in order to evaluate the physical, optical and electrical features involved in determining the catalytic degradation of rhodamine B and phenol in water.Several properties of native ZnO structures were found changed upon the rGO enwrapping (including optical absorbance, concentration of native defects in the ZnO matrix and double-layer capacitance), which are all involved in determining the photocatalytic performance of the hybrid composites. The findings discussed in the present work highlight the high complexity of the field of application of graphene-derivatives as supporters of semiconducting metal oxides functionality, which has to be analyzed through a multi-parametric approach.     

The Unbearable Shallow Understanding of Deep Learning

This paper analyzes the rapid and unexpected rise of deep learning within Artificial Intelligence and its applications. It tackles the possible reasons for this remarkable success, providing candidate paths towards a satisfactory explanation of why it works so well, at least in some domains. A historical account is given for the ups and downs, which have characterized neural networks research and its evolution from “shallow” to “deep” learning architectures. A precise account of “success” is given, in order to sieve out aspects pertaining to marketing or sociology of research, and the remaining aspects seem to certify a genuine value of deep learning, calling for explanation. The alleged two main propelling factors for deep learning, namely computing hardware performance and neuroscience findings, are scrutinized, and evaluated as relevant but insufficient for a comprehensive explanation. We review various attempts that have been made to provide mathematical foundations able to justify the efficiency of deep learning, and we deem this is the most promising road to follow, even if the current achievements are too scattered and relevant for very limited classes of deep neural models. The authors’ take is that most of what can explain the very nature of why deep learning works at all and even very well across so many domains of application is still to be understood and further research, which addresses the theoretical foundation of artificial learning, is still very much needed.

     

Feasibility of glioblastoma tissue response mapping with physiologic BOLD imaging using precise oxygen and carbon dioxide challenge

Magnetic Resonance Materials in Physics, Biology and Medicine - Innovative physiologic MRI development focuses on depiction of heterogenous vascular and metabolic features in glioblastoma. For this...     

The ProteoMiner and the FortyNiners: searching for gold nuggets in the proteomic arena

The present review covers modern aspects of combinatorial peptide ligand libraries (CPLL), as used to analyze the "low-abundance proteome" in association with mass spectrometry. First, the capturing properties of baits of different lengths (from single amino acid to hexa-peptides) are described to show that a plateau is rapidly reached above a tetra-peptide in length, thus confirming the validity of having adopted hexapeptides for the considered application. The mechanism of interaction with proteins from very complex proteomes and the ability to decrease the dynamic concentration range is demonstrated with the help of mass spectrometry analysis. Examples are given on how treatment with CPLLs dramatically improves the detectability of peptides in mass spectrometry analysis, permitting detection of a very large number of proteins as compared with control, untreated samples. The use of complementary libraries is discussed with the aim to discover additional low-abundance species that escaped the first library. A discussion on the possibility to discover extremely rare gene products, and the quantitative aspect of the technology when associated with mass spectrometry is also provided. Some insights on the applications for hidden, low-abundance biomarkers are also presented.     

Growth factors, CD34 positive cells, and fibrin network analysis in concentrated growth factors fraction

An interesting clinical option for optimizing healing tissue is the use of platelet concentrate. Platelets contain high quantities of growth factors, among these TGF-β1 and VEGF, which are known to be implicated in tissue regeneration. CGF is produced by processing blood samples with a special centrifuge device; three layers are formed: top acellular plasma (PPP), middle CGF and bottom red blood cells (RBC) layers. Given that to date there are no data concerning the biological characteristic of CGF, the aim of this study was to evaluate the presence of TGF-β1 and VEGF in CGF and also in PPP and RBC layers. In addition, since circulating stem cells are recruited from blood to injured tissue for healing we also evaluated the presence of CD34 positive cells. Our data show the presence of TGF-β1 and VEGF in CGF and RBC layers. In addition, we show CD34 positive cells in CGF.