Estimates of intakes: methodology and results of some studies carried out in Italy.

The methods which may be applied to estimating population intakes of food additives are outlined. The applications in studies of the intakes of some preservatives and food colours in Italy are reported. It was concluded that there is a need to investigate comparability between the various methods used. The surveys carried out indicated that, in general, the levels of consumption of the additives studied did not present a health risk.     

A new framework for sign language alphabet hand posture recognition using geometrical features through artificial neural network (part 1)

Neural Computing and Applications - Hand pose tracking is essential in sign languages. An automatic recognition of performed hand signs facilitates a number of applications, especially for people...     

Decreased resistance to intravenous tumour-cell challenge during reticuloendothelial depression following surgery

The influence of surgical stress on resistance to i.v. challenge with Walker 256 tumour cells was investigated in rats, with respect to the functional state of the reticuloendothelial system (RES). Phagocytic activity of the RES was evaluated by colloid (gelatinized [131I] "RE test lipid emulsion") clearance, and opsonin levels were determined by bioassay. Reticuloendothelial clearance capacity was significantly (P less than 0-05) depressed 60 min following surgery (coeliotomy plus jejunal enterotomy) as quantified by both humoral and cellular parameters of RE function. Phagocytic depression was primarily due to impaired hepatic Kupffer cell function and related to a deficiency in the phagocytic supporting capacity of plasma, also referred to as opsonic or recognition factor (RF) capacity. During the postoperative period of RES colloid clearance depression, pulmonary localization of the blood-borne test particulate matter increased. Rats challenged with 51Cr-labelled viable tumour cells at a dose of 1-0 X 106 i.v., either prior to or during the postoperative period of RE depression, manifested a significant (P less than 0-05) increment in pulmonary localization of the viable tumour cells, and a decrease (P less than 0-05) in hepatic clearance. Evaluation of survival patterns demonstrated a significant (P less than 0-01) decrease in host resistance to i.v. tumour cell challenge (2 X 103 cells) during the postoperative period of RE depression and hypo-opsonaemia. Sham-anaesthetized control animals survived 17-9 +/- 0-8 days, while animals challenged during the period of RE depression survived 7-9 +/- 0-4 days. An increased incidence of respiratory distress and nasal discharge was observed in the animals with impaired survival. Thus, surgical manipulation may transiently compromise RES systemic host defence and may be reflected in an increment in the pulmonary localization of blood-borne tumour cells. The relationship of this altered pattern of tumour cell distribution to the impaired survival remains to be determined, and warrants investigations.     

Metasurfaces Atop Metamaterials: Surface Morphology Induces Linear Dichroism in Gyroid Optical Metamaterials

Optical metamaterials offer the tantalizing possibility of creating extraordinary optical properties through the careful design and arrangement of subwavelength structural units. Gyroid‐structured optical metamaterials possess a chiral, cubic, and triply periodic bulk morphology that exhibits a redshifted effective plasma frequency. They also exhibit a strong linear dichroism, the origin of which is not yet understood. Here, the interaction of light with gold gyroid optical metamaterials is studied and a strong correlation between the surface morphology and its linear dichroism is found. The termination of the gyroid surface breaks the cubic symmetry of the bulk lattice and gives rise to the observed wavelength‐ and polarization‐dependent reflection. The results show that light couples into both localized and propagating plasmon modes associated with anisotropic surface protrusions and the gaps between such protrusions. The localized surface modes give rise to the anisotropic optical response, creating the linear dichroism. Simulated reflection spectra are highly sensitive to minute details of these surface terminations, down to the nanometer level, and can be understood with analogy to the optical properties of a 2D anisotropic metasurface atop a 3D isotropic metamaterial. This pronounced sensitivity to the subwavelength surface morphology has significant consequences for both the design and application of optical metamaterials.     

Single-Circulating Tumor Cell Whole Genome Amplification to Unravel Cancer Heterogeneity and Actionable Biomarkers

The field of single-cell analysis has advanced rapidly in the last decade and is providing new insights into the characterization of intercellular genetic heterogeneity and complexity, especially in human cancer. In this regard, analyzing single circulating tumor cells (CTCs) is becoming particularly attractive due to the easy access to CTCs from simple blood samples called “liquid biopsies”. Analysis of multiple single CTCs has the potential to allow the identification and characterization of cancer heterogeneity to guide best therapy and predict therapeutic response. However, single-CTC analysis is restricted by the low amounts of DNA in a single cell genome. Whole genome amplification (WGA) techniques have emerged as a key step, enabling single-cell downstream molecular analysis. Here, we provide an overview of recent advances in WGA and their applications in the genetic analysis of single CTCs, along with prospective views towards clinical applications. First, we focus on the technical challenges of isolating and recovering single CTCs and then explore different WGA methodologies and recent developments which have been utilized to amplify single cell genomes for further downstream analysis. Lastly, we list a portfolio of CTC studies which employ WGA and single-cell analysis for genetic heterogeneity and biomarker detection.     

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.     

Improved quadtree image segmentation approach to region information

Images are full of information and most often, little information is desired for subsequent processing. Hence, region of interest has key importance in image processing. Quadtree image segmentation has been widely used in many image processing applications to locate the region of interest for further processing. There are also variable block-size image coding techniques to effectively reduce the number of transmitted parts. This paper presents quadtree partition technique as a pre-processing step in image processing to determine what part should be more heterogeneous than the others. It also introduces an idea to solve the problem of squared images. Finally, proposed approach is implemented and analysed. The simulation of the Matlab code of the quadtree is represented by all algorithms and the figures. Thus, achieved results are promising in the state of the art.     

Exploring network-level performances of wireless nanonetworks utilizing gains of different types of nano-antennas with different materials

Wireless nanonetworks are not a simple extension of traditional communication networks at the nano-scale. Owing to being a completely new communication paradigm, existing research in this field is still at an embryonic stage. Furthermore, most of the existing studies focus on performance enhancement of nanonetworks via designing new channel models and routing protocols. However, the impacts of different types of nano-antennas on the network-level performances of the wireless nanonetworks remain still unexplored in the literature. Therefore, in this paper, we explore the impacts of different well-known types of antennas such as patch, dipole, and loop nano-antennas on the network-level performances of wireless nanonetworks. We also investigate the performances of nanonetworks for different types of traditional materials (e.g., copper) and for nanomaterials (e.g., carbon nanotubes and graphene). We perform rigorous simulation using our customized ns-2 simulation to evaluate the network-level performances of nanonetworks exploiting different types of nano-antennas using different materials. Our evaluation reveals a number of novel findings pertinent to finding an efficient nano-antenna from its several alternatives for enhancing network-level performances of nanonetworks. Our evaluation demonstrates that a dipole nano-antenna using copper material exhibits around 51% better throughput and about 33% better end-to-end delay compared to other alternatives for large-size nanonetworks. Furthermore, our results are expected to exhibit high impacts on the future design of wireless nanonetworks through facilitating the process of finding the suitable type of nano-antenna and suitable material for the nano-antennas.

     

Magnetic resonance image denoising using nonlocal maximum likelihood paradigm in DCT‐framework

The data acquired by magnetic resonance (MR) imaging system are inherently degraded by noise that has its origin in the thermal Brownian motion of electrons. Denoising can enhance the quality (by improving the SNR) of the acquired MR image, which is important for both visual analysis and other post processing operations. Recent works on maximum likelihood (ML) based denoising shows that ML methods are very effective in denoising MR images and has an edge over the other state‐of‐the‐art methods for MRI denoising. Among the ML based approaches, the Nonlocal maximum likelihood (NLML) method is commonly used. In the conventional NLML method, the samples for the ML estimation of the unknown true pixel are chosen in a nonlocal fashion based on the intensity similarity of the pixel neighborhoods. Euclidean distance is generally used to measure this similarity. It has been recently shown that computing similarity measure is more robust in discrete cosine transform (DCT) subspace, compared with Euclidean image subspace. Motivated by this observation, we integrated DCT into NLML to produce an improved MRI filtration process. Other than improving the SNR, the time complexity of the conventional NLML can also be significantly reduced through the proposed approach. On synthetic MR brain image, an average improvement of 5% in PSNR and 86%reduction in execution time is achieved with a search window size of 91 × 91 after incorporating the improvements in the existing NLML method. On an experimental kiwi fruit image an improvement of 10% in PSNR is achieved. We did experiments on both simulated and real data sets to validate and to demonstrate the effectiveness of the proposed method. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 256–264, 2015     

An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach

Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for highly equipped environment. The recent advancements in computerized solutions for these diagnoses are highly promising with improved accuracy and efficiency. In this article, we proposed a method for the classification of melanoma and benign skin lesions. Our approach integrates preprocessing, lesion segmentation, features extraction, features selection, and classification. Preprocessing is executed in the context of hair removal by DullRazor, whereas lesion texture and color information are utilized to enhance the lesion contrast. In lesion segmentation, a hybrid technique has been implemented and results are fused using additive law of probability. Serial based method is applied subsequently that extracts and fuses the traits such as color, texture, and HOG (shape). The fused features are selected afterwards by implementing a novel Boltzman Entropy method. Finally, the selected features are classified by Support Vector Machine. The proposed method is evaluated on publically available data set PH2. Our approach has provided promising results of sensitivity 97.7%, specificity 96.7%, accuracy 97.5%, and F‐score 97.5%, which are significantly better than the results of existing methods available on the same data set. The proposed method detects and classifies melanoma significantly good as compared to existing methods.