Tamquam alter idem: formal similarities in a subset of reports on anti-inflammatory compounds in the years 2008–2019

Scientometrics - A literature search on the in vitro testing of anti-inflammatory compounds of natural origin revealed a considerable number of studies adopting a similar template for data...     

Role of Extracellular Vesicles in Epithelial Ovarian Cancer: A Systematic Review

Extracellular vesicles (EVs) are a heterogeneous group of cell-derived submicron vesicles released under physiological or pathological conditions. EVs mediate the cellular crosstalk, thus contributing to defining the tumor microenvironment, including in epithelial ovarian cancer (EOC). The available literature investigating the role of EVs in EOC has been reviewed following PRISMA guidelines, focusing on the role of EVs in early disease diagnosis, metastatic spread, and the development of chemoresistance in EOC. Data were identified from searches of Medline, Current Contents, PubMed, and from references in relevant articles from 2010 to 1 April 2020. The research yielded 194 results. Of these, a total of 36 papers, 9 reviews, and 27 original types of research were retained and analyzed. The literature findings demonstrate that a panel of EV-derived circulating miRNAs may be useful for early diagnosis of EOC. Furthermore, it appears clear that EVs are involved in mediating two crucial processes for metastatic and chemoresistance development: the epithelial–mesenchymal transition, and tumor escape from the immune system response. Further studies, more focused on in vivo evidence, are urgently needed to clarify the role of EV assessment in the clinical management of EOC patients.     

A Simple and Effective Mass Spectrometric Approach to Identify the Adulteration of the Mediterranean Diet Component Extra-Virgin Olive Oil with Corn Oil

Extra virgin olive oil (EVOO) with its nutraceutical characteristics substantially contributes as a major nutrient to the health benefit of the Mediterranean diet. Unfortunately, the adulteration of EVOO with less expensive oils (e.g., peanut and corn oils), has become one of the biggest source of agricultural fraud in the European Union, with important health implications for consumers, mainly due to the introduction of seed oil-derived allergens causing, especially in children, severe food allergy phenomena. In this regard, revealing adulterations of EVOO is of fundamental importance for health care and prevention reasons, especially in children. To this aim, effective analytical methods to assess EVOO purity are necessary. Here, we propose a simple, rapid, robust and very sensitive method for non-specialized mass spectrometric laboratory, based on the matrix-assisted laser desorption/ionization mass spectrometry (MALDI-TOF MS) coupled to unsupervised hierarchical clustering (UHC), principal component (PCA) and Pearson’s correlation analyses, to reveal corn oil (CO) adulterations in EVOO at very low levels (down to 0.5%).     

Logarithmic wavelength demultiplexers

A general approach for a full 1 /spl times/ N demultiplexer using a tree of filter stages is proposed. The device architecture is compact, requiring at least N - 1 filter stages, and flexible, as each filter stage can be arbitrarily designed, with the only constraint of the half-band power property. The filters can be realized using any optical filtering techniques as thin-film interference, Bragg gratings, or planar delay-line circuits. The performances of the proposed architecture are illustrated with respect to different lattice-form finite impulse response (FIR) and infinite impulse response (IIR) filter stages, showing that the demultiplexer inherently presents low crosstalk and flat passband. A design example of a 1 /spl times/ 4 demultiplexer consisting of three all-pass (AP) filters is compared with a generalized Mach-Zehnder interferometer (MZI) with four AP filters in its arms, showing that the two approaches achieve similar results.     

QoS assessment of 3G video-phone calls by tracing watermarking exploiting the new colour space `YST?

Tracing watermarking has been recently proposed as a technique to provide a blind measure of the quality of service of the communication link, focusing on multimedia communication scenarios. 3G communication scenarios, where each customer transmits both voice and MPEG-4 video sequences in real time, have been considered. Typical video-phone call consists of close shots of speakers, framing their face in the foreground. For such particular application, a novel colour space (YST) is adopted to minimise the perceptual distortions on face coding introduced by watermarking. In the YST space, the luminance component (Y) is the same as in conventional YUV space, whereas the vectors S and T lie within the chrominance (UV) plane. The S (skin) component is a linear combination of U and V obtained as the 'average' chrominance estimated from an assorted set human faces to achieve a reasonable generalisation. The third component, T, is defined as orthogonal to the YS plane. The results show the benefits obtained in digital watermarking by the new representation against the conventional approach. In fact, the sensitivity of the YST representation outperforms the conventional one in terms of objective (mean square error, peak signal-to-noise ratio) and subjective (video-quality metrics) indicators.     

Anisotropic mesh adaption by metric‐driven optimization

We describe a Gauss–Seidel algorithm for optimizing a three‐dimensional unstructured grid so as to conform to a given metric. The objective function for the optimization process is based on the maximum value of an elemental residual measuring the distance of any simplex in the grid to the local target metric. We analyse different possible choices for the objective function, and we highlight their relative merits and deficiencies. Alternative strategies for conducting the optimization are compared and contrasted in terms of resulting grid quality and computational costs. Numerical simulations are used for demonstrating the features of the proposed methodology, and for studying some of its characteristics. Copyright © 2004 John Wiley & Sons, Ltd.     

Hidden Markov modeling using a dominant state sequence with application to speech recognition

Approximate maximum likelihood (ML) hidden Markov modeling using the most likely state sequence (MLSS) is examined and compared with the exact ML approach that considers all possible state sequences. It is shown that for any hidden Markov model (HMM), the difference between the approximate and the exact normalized likelihood functions cannot exceed the logarithm of the number of states divided by the dimension of the output vectors (frame length), which is negligible for typically used values of vector dimension (128–256) and number of states (2–30). Furthermore, for Gaussian HMMs and a given observation sequence, the MLSS is typically the sequence of nearest neighbor states in the Itakura-Saito sense, and the posterior probability of any state sequence which departs from the MLSS in a single time instant decays exponentially with the frame length. Hence, for a sufficiently large frame length the exact and approximate ML approaches provide similar model estimates and likelihood values. The results and their implications on speech recognition are demonstrated in a set of experiments.