Iodine from brown algae in human nutrition,with an emphasis on bioaccessibility,bioavailability, chemistry,and effects of processing: A systematic review

Brown algae are becoming increasingly popular as a food source and dietary supplement in Europe and other Western countries. As they are highly rich in iodine, they represent a potential new dietary iodine source. Iodine deficiency has been re-emerging in Europe, and it is important to ensure adequate intake through one's diet. However, macroalgae, and especially brown algae, may contain very high amounts of iodine, and both iodine deficiency and excessive iodine may increase the risk of negative health effects. The iodine content of algae or foods containing algae is currently not regulated in the European Union. The aim of this paper is to review the literature to determine the chemical species of iodine in brown algae, the loss of iodine during processing, and the bioavailability and bioaccessibility of iodine. A systematic search of the literature was performed in April 2021, via the databases Web of Science and PubMed. The review includes studies of iodine in brown macroalgae in relation to bioavailability, bioaccessibility, processing and speciation. A meta-analysis was conducted in relation to the following topics: (i) the correlation between total iodine and iodide (I^–) content in brown algae; (ii) the correlation between the loss of iodine during processing and the I^– content; and (iii) the correlation between bioavailability and the I^– content. The bioavailability of iodine from brown algae was generally high, with in vivo bioavailability ranging from 31% to 90%. The in vitro bioavailability of iodine (2%–28%) was systematically lower than in vivo bioavailability (31%–90%), indicating an inadequate in vitro methodology. Processing may reduce the iodine content of brown algae, and a higher I^– content was positively correlated with increased iodine loss during processing. Although processing strategies may reduce the iodine content of brown algae significantly, the iodine content may still be high after processing. These findings may be used in food safety evaluations of brown algae as well as in the development of macroalgae-containing foods with iodine contents suitable for human consumption. Further research on processing techniques to reduce the iodine content in brown macroalgae are warranted.     

Defocusing image to pattern contact holes using attenuated phase shift masks

The patterning of contact holes by selecting out-of-focus image plane (defocus) using attenuated phase shift masks (APSM) has been studied. Defocus is found to enhance the image modulation at low partial coherence for contact holes with negative local average of mask function. Semi-dense holes up to 130 nm in 8% APSM have been printed by 0.5 μm defocus at a partial coherence of 0.31 using KrF scanner with highest numerical aperture of 0.68. However, these holes were closed with in-focus imaging. Defocus is also found to be beneficial for patterning the pitches that have extensive side lobes with in-focus imaging.     

A stochastic realization algorithm via block LQ decomposition in Hilbert space

A stochastic realization problem of a stationary stochastic process is re-visited, and a new stochastically balanced realization algorithm is derived in a Hilbert space generated by second-order stationary processes. The present algorithm computes a stochastically balanced realization by means of the singular value decomposition of a weighted block Hankel matrix derived by a “block LQ decomposition”. Extension to a stochastic subspace identification method explains how the proposed abstract algorithm is implemented in system identification.     

Pedestrian detection and tracking at crossroads

This paper presents a system that can perform pedestrian detection and tracking using vision-based techniques. A very important issue in the field of intelligent transportation system is to prevent pedestrians from being hit by vehicles. Recently, a great number of vision-based techniques have been proposed for this purpose. In this paper, we propose a vision-based method, which combines the use of a pedestrian model as well as the walking rhythm of pedestrians to detect and track walking pedestrians. Through integrating some spatial and temporal information grabbed by a vision system, we are able to develop a reliable system that can be used to prevent traffic accidents happened at crossroads. In addition, the proposed system can deal with the occlusion problem. Experimental results obtained by executing some real world cases have demonstrated that the proposed system is indeed superb.     

An immune oriented multi-agent system for biological image processing

In this article, we present a parallel image processing system based on the concept of reactive agents. Our system lies in the oRis language, which allows to describe finely and simply the agents’ behaviors to detect image features. We also present a method of segmentation using a multi-agent system, and two biological applications made with oRis. The stopping of this multi-agent system is implemented through a technique issued from immunology: the apoptosis.     

Frequency domain identification of Wiener models

This paper proposes a frequency domain algorithm for Wiener model identifications based on exploring the fundamental frequency and harmonics generated by the unknown nonlinearity. The convergence of the algorithm is established in the presence of white noise. No a priori knowledge of the structure of the nonlinearity is required and the linear part can be nonparametric.     

Frequency domain solution to delay-type Nehari problem

This paper generalizes the frequency-domain results on the delay-type Nehari problem in the stable case to the unstable case. The solvability condition of the delay-type Nehari problem is formulated in terms of the nonsingularity of three matrices. The optimal value γ_opt is the maximal γ∈(0,∞) such that one of the three matrices becomes singular. All sub-optimal compensators are parameterized in a transparent structure incorporating a modified Smith predictor.     

Directional features in online handwriting recognition

The selection of valuable features is crucial in pattern recognition. In this paper we deal with the issue that part of features originate from directional instead of common linear data. Both for directional and linear data a theory for a statistical modeling exists. However, none of these theories gives an integrated solution to problems, where linear and directional variables are to be combined in a single, multivariate probability density function. We describe a general approach for a unified statistical modeling, given the constraint that variances of the circular variables are small. The method is practically evaluated in the context of our online handwriting recognition system frog on hand and the so-called tangent slope angle feature. Recognition results are compared with two alternative modeling approaches. The proposed solution gives significant improvements in recognition accuracy, computational speed and memory requirements.