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

A Span-based Model for Extracting Overlapping PICO Entities from RCT Publications

Objectives Extraction of PICO (Populations, Interventions, Comparison, and Outcomes) entities is fundamental to evidence retrieval. We present a novel method PICOX to extract overlapping PICO entities. Materials and Methods PICOX first identifies entities by assessing whether a word marks the beginning or conclusion of an entity. Then it uses a multi-label classifier to assign one or more PICO labels to a span candidate. PICOX was evaluated using one of the best-performing baselines, EBM-NLP, and three more datasets, i.e., PICO-Corpus, and RCT publications on Alzheimer's Disease or COVID-19, using entity-level precision, recall, and F1 scores. Results PICOX achieved superior precision, recall, and F1 scores across the board, with the micro F1 score improving from 45.05 to 50.87 (p << 0.01). On the PICO-Corpus, PICOX obtained higher recall and F1 scores than the baseline and improved the micro recall score from 56.66 to 67.33. On the COVID-19 dataset, PICOX also outperformed the baseline and improved the micro F1 score from 77.10 to 80.32. On the AD dataset, PICOX demonstrated comparable F1 scores with higher precision when compared to the baseline. Conclusion PICOX excels in identifying overlapping entities and consistently surpasses a leading baseline across multiple datasets. Ablation studies reveal that its data augmentation strategy effectively minimizes false positives and improves precision

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