Theoretical foundations of studying criticality in the brain

Criticality is hypothesized as a physical mechanism underlying efficient transitions between cortical states and remarkable information processing capacities in the brain. While considerable evidence generally supports this hypothesis, non-negligible controversies persist regarding the ubiquity of criticality in neural dynamics and its role in information processing. Validity issues frequently arise during identifying potential brain criticality from empirical data. Moreover, the functional benefits implied by brain criticality are frequently misconceived or unduly generalized. These problems stem from the non-triviality and immaturity of the physical theories that analytically derive brain criticality and the statistic techniques that estimate brain criticality from empirical data. To help solve these problems, we present a systematic review and reformulate the foundations of studying brain criticality, i.e., ordinary criticality (OC), quasi-criticality (qC), self-organized criticality (SOC), and self-organized quasi-criticality (SOqC), using the terminology of neuroscience. We offer accessible explanations of the physical theories and statistic techniques of brain criticality, providing step-by-step derivations to characterize neural dynamics as a physical system with avalanches. We summarize error-prone details and existing limitations in brain criticality analysis and suggest possible solutions. Moreover, we present a forward-looking perspective on how optimizing the foundations of studying brain criticality can deepen our understanding of various neuroscience questions

Cell Spatial Analysis in Crohn's Disease: Unveiling Local Cell Arrangement Pattern with Graph-based Signatures

Crohn's disease (CD) is a chronic and relapsing inflammatory condition that affects segments of the gastrointestinal tract. CD activity is determined by histological findings, particularly the density of neutrophils observed on Hematoxylin and Eosin stains (H&E) imaging. However, understanding the broader morphometry and local cell arrangement beyond cell counting and tissue morphology remains challenging. To address this, we characterize six distinct cell types from H&E images and develop a novel approach for the local spatial signature of each cell. Specifically, we create a 10-cell neighborhood matrix, representing neighboring cell arrangements for each individual cell. Utilizing t-SNE for non-linear spatial projection in scatter-plot and Kernel Density Estimation contour-plot formats, our study examines patterns of differences in the cellular environment associated with the odds ratio of spatial patterns between active CD and control groups. This analysis is based on data collected at the two research institutes. The findings reveal heterogeneous nearest-neighbor patterns, signifying distinct tendencies of cell clustering, with a particular focus on the rectum region. These variations underscore the impact of data heterogeneity on cell spatial arrangements in CD patients. Moreover, the spatial distribution disparities between the two research sites highlight the significance of collaborative efforts among healthcare organizations. All research analysis pipeline tools are available at https://github.com/MASILab/cellNN.Comment: Submitted to SPIE Medical Imaging. San Diego, CA. February 202

A population-based phenome-wide association study of cardiac and aortic structure and function

Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart–brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers

A systematic progress model for construction method innovation

The rapid development of Construction Method has posed a challenge for construction industry. This paper utilizes current Technological Innovation Method to accelerate the development of Construction Method. The Rapid Innovation Model is established based on the traditional technology innovation. This model contains four parts which can be divided into nine steps: definition of problem, fundamental reason analysis, selection of target technique, functional model analysis, scheme evaluation, experiment method, effect evaluation, summary and further application. Moreover, this paper introduces the process of SPIP Method which contains three phases: interpreting the problem according to basic reasons, seeking the answer by patent analysis and construction model and getting the solution by TRIZ and invention principle. In summary, the rapid innovation model meets both the speed and the quality requirements of Construction model development, pointing out a new way of developing construction model.Non UBCUnreviewedFacultyOthe

Collinear micro-shear-bands model for grain-size and precipitate-size effects on the yield strength

ABSTRACT: Numerous experimental evidences show that the grain size may significantly alter the yield strength of metals. Similarly, in γ‘-strengthened nickel-based superalloys, the precipitate size also influences their yield strength. Then, how to describe such two kinds of size effects on the yield strength is a very practical challenge. In this study, according to experimental observations, a collinear micro-shear-bands model is proposed to explore these size effects on metal materials’ yield strength. An analytical solution for the simple model is derived. It reveals that the yield strength is a function of average grain-size or precipitate-size, which is able to reasonably explain size effects on yield strength. The typical example validation shows that the new relationship is not only able to precisely describe the grain-size effect in some cases, but also able to theoretically address the unexplained Hall-Petch relationship between the γ’ precipitate size and the yield strength of nickel-based superalloys. Keywords: Collinear micro-shear-bands model, The Hall-Petch relationship, γ‘ precipitate, Size effec

Near-infrared radiation absorption properties of covellite (CuS) using first-principles calculations

First-principles density functional theory was used to investigate the electronic structure, optical properties and the origin of the near-infrared (NIR) absorption of covellite (CuS). The calculated lattice constant and optical properties are found to be in reasonable agreement with experimental and theoretical findings. The electronic structure reveals that the valence and conduction bands of covellite are determined by the Cu 3d and S 3p states. By analyzing its optical properties, we can fully understand the potential of covellite (CuS) as a NIR absorbing material. Our results show that covellite (CuS) exhibits NIR absorption due to its metal-like plasma oscillation in the NIR range

Rational Design and Mechanical Understanding of Three-Dimensional Macro-/Mesoporous Silicon Lithium-Ion Battery Anodes with a Tunable Pore Size and Wall Thickness

Silicon is regarded as one of the most promising next generation lithium-ion battery anodes due to its exceptional theoretical capacity, appropriate voltage profile, and vast abundance. Nevertheless, huge volume expansion and drastic stress generated upon lithiation cause poor cyclic stability. It has been one of the central issues to improve cyclic performance of silicon-based lithium-ion battery anodes. Constructing hierarchical macro-/mesoporous silicon with a tunable pore size and wall thickness is developed to tackle this issue. Rational structure design, controllable synthesis, and theoretical mechanical simulation are combined together to reveal fundamental mechanisms responsible for an improved cyclic performance. A self-templating strategy is applied using Stober silica particles as a templating agent and precursor coupled with a magnesiothermic reduction process. Systematic variation of the magnesiothermic reduction time allows good control over the structures of the porous silicon. Finite element mechanical simulations on the porous silicon show that an increased pore size and a reduced wall thickness generate less mechanical stress in average along with an extended lithiation state. Besides the mechanical stress, the evolution of strain and displacement of the porous silicon is also elaborated with the finite element simulation

Additional file 9: of Analysis of transcriptome in hickory (Carya cathayensis), and uncover the dynamics in the hormonal signaling pathway during graft process

The expression level of cytokinin signaling pathway related genes. (XLSX 11 kb

Additional file 5: of Analysis of transcriptome in hickory (Carya cathayensis), and uncover the dynamics in the hormonal signaling pathway during graft process

The information of the classification of the KEGGs. (XLSX 9 kb