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This article presents a model-based eddy current (EC) testing method for the reconstruction of geometrical features, that may be hidden, through, or blind cavity in a large electrically conductive plate. Based on a distributed current source (DCS) modeling method, a perturbation model is formulated to convert the feature reconstruction problem into finding a possibility vector using a finite set of measured magnetic flux density (MFD) data and solved with the Tikhonov regularization method. The proposed method is numerically and experimentally validated and demonstrated by using the specimens with compound conductive/nonconductive geometrical features and comparing them with two published feature detection methods. Parametric studies are conducted to analyze the effects of feature shape, sensor configuration, and multi-coil excitation on reconstruction accuracy. [ABSTRACT FROM AUTHOR]

Multicellularity was accompanied by the emergence of new classes of cell surface and secreted proteins. The nematode C. elegans is a favorable model to study cell surface interactomes, given its well-defined and stereotyped cell types and intercellular contacts. Here we report our C. elegans extracellular interactome dataset, the largest yet for an invertebrate. Most of these interactions were unknown, despite recent datasets for flies and humans, as our collection contains a larger selection of protein families. We uncover new interactions for all four major axon guidance pathways, including ectodomain interactions between three of the pathways. We demonstrate that a protein family known to maintain axon locations are secreted receptors for insulins. We reveal novel interactions of cystine-knot proteins with putative signaling receptors, which may extend the study of neurotrophins and growth-factor-mediated functions to nematodes. Finally, our dataset provides insights into human disease mechanisms and how extracellular interactions may help establish connectomes.

Social ties, either positive or negative, lead to signed network patterns, the subject of balance theory. For example, strong balance introduces cycles with even numbers of negative edges. The statistical significance of such patterns is routinely assessed by comparisons to null models. Yet, results in signed networks remain controversial. Here, we show that even if a network exhibits strong balance by construction, current null models can fail to identify it. Our results indicate that matching the signed degree preferences of the nodes is a critical step and so is the preservation of network topology in the null model. As a solution, we propose the STP null model, which integrates both constraints within a maximum entropy framework. STP randomization leads to qualitatively different results, with most social networks consistently demonstrating strong balance in three- and four-node patterns. On the basis our results, we present a potential wiring mechanism behind the observed signed patterns and outline further applications of STP randomization.

Due to molecular forces, biomacromolecules assemble into liquid condensates or solid aggregates, and their corresponding formation and dissolution processes are controlled. Protein homeostasis is disrupted by increasing age or environmental stress, leading to irreversible protein aggregation. Hypoxic pressure is an important factor in this process, and uncontrolled protein aggregation has been widely observed in hypoxia‑related conditions such as neurodegenerative disease, cardiovascular disease, hypoxic brain injury and cancer. Biomolecular condensates are also high‑order complexes assembled from macromolecules. Although they exist in different phase from protein aggregates, they are in dynamic balance under certain conditions, and their activation or assembly are considered as important regulatory processes in cell survival with hypoxic pressure. Therefore, a better understanding of the relationship between hypoxic stress, protein aggregation and biomolecular condensation will bring marked benefits in the clinical treatment of various diseases. The aim of the present review was to summarize the underlying mechanisms of aggregate assembly and dissolution induced by hypoxic conditions, and address recent breakthroughs in understanding the role of aggregates in hypoxic‑related diseases, given the hypotheses that hypoxia induces macromolecular assemblage changes from a liquid to a solid phase, and that adenosine triphosphate depletion and ATP‑driven inactivation of multiple protein chaperones play important roles among the process. Moreover, it is anticipated that an improved understanding of the adaptation in hypoxic environments could extend the overall survival of patients and provide new strategies for hypoxic‑related diseases.