The DONALD study as a longitudinal sensor of nutritional developments: iodine and salt intake over more than 30 years in German children

Purpose: Mild-to-moderate iodine deficiency was present in large parts of Germany up to the beginning 1990s and improved from then on. Current epidemiological data on spot urine iodine measurements in German children strongly suggest the re-occurrence of an impaired iodine status. We thus examined whether this re-occurrence is identifiable in more detail, through iodine analyses of 24-h urine samples of a well-characterized cohort of German children in whom samples have been systematically collected from 1985 onward. As iodized salt is a major source for iodine supply, urinary sodium excretion was additionally studied. Methods: Daily iodine and sodium excretions were measured in 2600 24-h urine samples collected between 1985 and 2018 by 677 healthy children aged 6–12 years (participants of the DONALD study). These data were compared with 24-h iodine and sodium excretion estimates obtained from spot urine samples collected in the representative German Health Interview and Examination Surveys for Children and Adolescents KiGGS-baseline (2003–2006) and KiGGS-wave-2 (2014–2017). Results: Between 1985 and1992, DONALD participants started with a median daily iodine excretion level of 40.1 µg/d. Then, during 1993–2003, iodine excretions mounted up to an approximate plateau (~ 84.8 µg/d). This plateau lasted until 2012. Thereafter, iodine concentrations started to decrease again resulting in a median iodine excretion of only 58.9 µg/d in 2018. Sodium excretion, however, had increased. The marked decrease in iodine status along with an abundant sodium excretion corresponded closely with nationwide KiGGS data. Conclusions: As exemplified for the clearly worsening iodine status in German children, longitudinal cohort studies collecting detailed biomarker-based prospective data have the potential to reliably capture health-relevant nutritional changes and trends, applicable on a more comprehensive and even representative population level.Peer Reviewe

Investigating Neural Substrates of Individual Independence and Interdependence Orientations via Efficiency-based Dynamic Functional Connectivity : A Machine Learning Approach

Fundings: Beihang University and Capital Medical University Advanced Innovation Center for Big DataBased Precision Medicine Plan; 10.13039/501100001809-National Natural Science Foundation of China; 10.13039/501100000275-Leverhulme Trust;Peer reviewedPostprin

A wavelet neural network model for spatio-temporal image processing and modeling

Spatio-temporal images are a class of complex dynamical systems that evolve over both space and time. Compared with pure temporal processes, the identification of spatio-temporal models from observed images is much more difficult and quite challenging. Starting with an assumption that there is no a priori information about the true model but only observed data are available, this work introduces a new type of wavelet network that utilizes the easy tractability and exploits the good properties of multiscale wavelet decompositions to represent the rules of the associated spatio-temporal evolutionary system. An application to a chemical reaction exhibiting a spatio-temporal evolutionary behaviour, is investigated to demonstrate the application of the proposed modeling and learning approaches.This work was supported in part by EPSRC under Grant: EP/I011056/1 and Platform Grant EP/H00453X/

The divided brain : Functional brain asymmetry underlying self-construal

Acknowledgments This research is partly supported by the National Natural Science Foundation of China (62071049, 61801026) & Capital Medical University Advanced Innovation Center for Big Data-Based Precision Medicine Plan (BHME-201907), and the Leverhulme Trust (RPG-2019-010).Peer reviewedPublisher PD

An improved memetic algorithm to solve the energy-efficient distributed flexible job shop scheduling problem with transportation and start-stop constraints

In the current global cooperative production environment, modern industries are confronted with intricate production plans, demanding the adoption of contemporary production scheduling strategies. Within this context, distributed manufacturing has emerged as a prominent trend. Manufacturing enterprises, especially those engaged in activities like automotive mold production and welding, are facing a significant challenge in managing a significant amount of small-scale tasks characterized by short processing times. In this situation, it becomes imperative to consider the transportation time of jobs between machines. This paper simultaneously considers the transportation time of jobs between machines and the start-stop operation of the machines, which is the first time to our knowledge. An improved memetic algorithm (IMA) is proposed to solve the multi-objective distributed flexible job shop scheduling problem (MODFJSP) with the goal of minimizing maximum completion time and energy consumption. Then, a new multi-start simulated annealing algorithm is proposed and integrated into the IMA to improve the exploration ability and diversity of the algorithm. Furthermore, a new multiple-initialization rule is designed to enhance the quality of the initial population. Additionally, four improved variable neighborhood search strategies and two energy-saving strategies are designed to enhance the search ability and reduce energy consumption. To verify the effectiveness of the IMA, we conducted extensive testing and comprehensive evaluation on 20 instances. The results indicate that, when faced with the MODFJSP, the IMA can achieve better solutions in almost all instances, which is of great significance for the improvement of production scheduling in intelligent manufacturing