Functional Hierarchy Research Articles

Different polysaccharide-enhanced probiotic and polyphenol dual-functional factor co-encapsulated microcapsules demonstrate acute colitis alleviation efficacy and food fortification

Probiotics and polyphenols have multiple bioactivities, and developing co-encapsulated microcapsules (CM) is a novel strategy to enhance their nutritional diversity. However, the development of CMs is challenged by complicated processing, single types, and unclear in vivo effects and applications. In this study, the co-microencapsulations of polyphenol and probiotic were constructed using pectin, alginate (WGCA@LK), and Fu brick tea polysaccharides (WGCF@LK), respectively, with chitosan-whey isolate proteins by layer-by-layer coacervation reaction, and their protective effects, in vivo effectiveness, and application potential were evaluated. WGCA@LK improved the encapsulation rate of polyphenols (42.41 %), and remained high viability of probiotics after passing through gastric acidic environment (8.79 ± 0.04 log CFU/g) and storage for 4 weeks (4.59 ± 0.06 log CFU/g). WGCF@LK exhibited the highest total antioxidant activity (19.40 ± 0.25 μmol/mL) and its prebiotic activity removed the restriction on probiotic growth. WGCA@LK showed strong in vitro colonic adhesion, but WGCF@LK promoted in vivo retention of probiotics at 48 h. WGCF@LK showed excellent anti-inflammatory effects and alleviated symptoms of acute colitis in mice. These findings provide unique insights into the fortification of probiotic-polyphenol CMs by different polysaccharides and the development of novel health foods with rich functional hierarchies and superior therapeutic effects.

XModNN: Explainable Modular Neural Network to Identify Clinical Parameters and Disease Biomarkers in Transcriptomic Datasets

The Explainable Modular Neural Network (XModNN) enables the identification of biomarkers, facilitating the classification of diseases and clinical parameters in transcriptomic datasets. The modules within XModNN represent specific pathways or genes of a functional hierarchy. The incorporation of biological insights into the architectural design reduced the number of parameters. This is further reinforced by the weighted multi-loss progressive training, which enables successful classification with a reduced number of replicates. The combination of this workflow with layer-wise relevance propagation ensures a robust post hoc explanation of the individual module contribution. Two use cases were employed to predict sex and neuroblastoma cell states, demonstrating that XModNN, in contrast to standard statistical approaches, results in a reduced number of candidate biomarkers. Moreover, the architecture enables the training on a limited number of examples, attaining the same performance and robustness as support vector machine and random forests. The integrated pathway relevance analysis improves a standard gene set overrepresentation analysis, which relies solely on gene assignment. Two crucial genes and three pathways were identified for sex classification, while 26 genes and six pathways are highly important to discriminate adrenergic–mesenchymal cell states in neuroblastoma cancer.

A telescopic independent component analysis on functional magnetic resonance imaging dataset

Abstract Brain function can be modeled as dynamic interactions between functional sources at different spatial scales, and each spatial scale can contain its functional sources with unique information, thus using a single scale may provide an incomplete view of brain function. This paper introduces a novel approach, termed “telescopic independent component analysis (TICA),” designed to construct spatial functional hierarchies and estimate functional sources across multiple spatial scales using fMRI data. The method employs a recursive independent component analysis (ICA) strategy, leveraging information from a larger network to guide the extraction of information about smaller networks. We apply our model to the default mode network (DMN), visual network (VN), and right frontoparietal network (RFPN). We investigate further on the DMN by evaluating the difference between healthy people and individuals with schizophrenia. We show that the TICA approach can detect the spatial hierarchy of the DMN, VN, and RFPN. In addition, the TICA revealed DMN-associated group differences between cohorts that may not be captured if we focus on a single-scale ICA. In sum, our proposed approach represents a promising new tool for studying functional sources.

Longitudinal hippocampal axis in large-scale cortical systems underlying development and episodic memory

The hippocampus is functionally specialized along its longitudinal axis with intricate interactions with cortical systems, which is crucial for understanding development and cognition. Using a well-established connectopic mapping technique on two large resting-state functional MRI datasets, we systematically quantified topographic organization of the hippocampal functional connectivity (hippocampal gradient) and its cortical interaction in developing brains. We revealed hippocampal functional hierarchy within the large-scale cortical brain systems, with the anterior hippocampus preferentially connected to an anterior temporal (AT) pathway and the posterior hippocampus embedded in a posterior medial (PM) pathway. We examined the developmental effects of the primary gradient and its whole-brain functional interaction. We observed increased functional specialization along the hippocampal long axis and found a general whole-brain connectivity shift from the posterior to the anterior hippocampus during development. Using phenotypic predictive modeling, we further delineated how the hippocampus is differentially integrated into the whole-brain cortical hierarchy underlying episodic memory and identified several key nodes within PM/AT systems. Our results highlight the importance of hippocampal gradient and its cortical interaction in development and for supporting episodic memory.

Integrating brainstem and cortical functional architectures.

The brainstem is a fundamental component of the central nervous system, yet it is typically excluded from in vivo human brain mapping efforts, precluding a complete understanding of how the brainstem influences cortical function. In this study, we used high-resolution 7-Tesla functional magnetic resonance imaging to derive a functional connectome encompassing cortex and 58 brainstem nuclei spanning the midbrain, pons and medulla. We identified a compact set of integrative hubs in the brainstem with widespread connectivity with cerebral cortex. Patterns of connectivity between brainstem and cerebral cortex manifest as neurophysiological oscillatory rhythms, patterns of cognitive functional specialization and the unimodal-transmodal functional hierarchy. This persistent alignment between cortical functional topographies and brainstem nuclei is shaped by the spatial arrangement of multiple neurotransmitter receptors and transporters. We replicated all findings using 3-Tesla data from the same participants. Collectively, this work demonstrates that multiple organizational features of cortical activity can be traced back to the brainstem.

Higher order constraints for the (β-deformed) Hermitian matrix models

We construct the (β-deformed) higher order total derivative operators and analyze their remarkable properties. In terms of these operators, we derive the higher order constraints for the (β-deformed) Hermitian matrix models. We prove that these (β-deformed) higher order constraints are reducible to the Virasoro constraints. Meanwhile, the Itoyama–Matsuo conjecture for the constraints of the Hermitian matrix model is proved. We also find that through rescaling variable transformations, two sets of the constraint operators become the W-operators of W-representations for the (β-deformed) partition function hierarchies in the literature.

ZeroD-fender: A Resource-aware IoT Malware Detection Engine via Fine-grained Side-channel Analysis

In early 2023, cyberattacks experienced a significant rise due to unknown (zero-day) malware targeting Internet of Things (IoT) devices. To tackle the challenge of zero-day detection within a highly resource-constrained IoT environment, we propose a novel design that utilizes fine-grained power side-channel analysis with deep learning techniques. Our approach introduces an innovative concept called multiscale feature extraction to identify the most representative malware features across diverse architectures, thereby enhancing deep learning based detection performance against zero-day malware. Specifically, we employ a fine-grained power side-channel analysis of more than 120,000 honeypot-collected malware files across a hierarchy of commands , functions , and modules to identify the unique zero-day malware behaviors. With these identified features to train our model, ZeroD-fender’s performance in detecting zero-day malware has significantly improved. In pursuit of on-device detection, we present a resource-aware online inference customization framework. This framework features our lightweight network, ThingNetV2, which uses specialized 1-D depthwise separable convolution paired with h-swish activation, leading to significant resource savings. By applying the fine-grained power analysis, ZeroD-fender demonstrates a detection rate of 95.88% across various architecture zero-day malware, achieving detection speeds ranging between 16.083 ms and 23.961 ms , depending on the specific scenario.

The anchoring policy perspective on policy change: mechanisms, applications and analytical challenges

ABSTRACT How functional relationships between policies influence policy design and policy change has received relatively limited attention within public policy scholarship despite the importance of policy coordination. This article contributes to addressing this gap by introducing the Anchoring Policies Perspective (APP), a framework for analysing interactions between policies in functional policy hierarchies. The APP examines how formal links between anchoring policies (AP) and functionally subordinate policies (SP) generate anchoring effects, whereby the provisions codified in APs generate alignment pressures that channel the design of SPs towards the norms and goals of the AP. In the article, we examine the analytical contribution of the APP by analysing the distinguishing characteristics of APs, the mechanisms through which APs influence SP introduction and design, and political strategies for managing situations where resistance occurs to the alignment of SPs with their APs. Examples from environmental and energy policy are used to illustrate key features of the AP-SP relationship, following which the APP’s contribution to public policy scholarship is discussed. We conclude by arguing that the APP has the potential to bring important new insights to the analysis of policy change while building connections with established theories of policy change to develop the APP framework further.

Identifying individual brain development using multimodality brain network

The cortical development of our brains is in a hierarchical manner and promotes the emergence of large-scale functional hierarchy. However, under interindividual heterogenicity, how the spatiotemporal features of brain networks reflect brain development and mental health remains unclear. Here we collect both resting-state electroencephalography and functional magnetic resonance imaging data from the Child Mind Institute Biobank to demonstrate that during brain growth, the global dynamic patterns of brain states become more active and the dominant networks shift from sensory to higher-level networks; the individual functional network patterns become more similar to that of adults and their spatial coupling tends to be invariable. Furthermore, the properties of multimodality brain networks are sufficiently robust to identify healthy brain age and mental disorders at specific ages. Therefore, multimodality brain networks provide new insights into the functional development of the brain and a more reliable and reasonable approach for age prediction and individual diagnosis.

Creating a Data Cleaning and Pre-Processing Module for Generalisable Data Linkage

ObjectiveThe Office for National Statistics (ONS) are developing a generalisable tool to facilitate the linkage of various datasets to its population-spine. However, a generalisable process requires that a variety of input datasets can be adaptively pre-processed – which is a problem for bespoke methodologies. Key requirements of the cleaning pipeline include minimal input from the user, and scalability and efficiency to work on Big Data. ApproachThe pipeline must recognise and adjust the pre-processing steps applied based on the variables present and user requirements. It must accept, preprocess, and derive consistent standardised variables from a variety of input variables and formats, including complex data characteristics. ResultsThe MVP pipeline successfully met the requirements. It is based on a three-level hierarchy of functions, allowing flexibility and complexity in data preparation. With minimal user input, a variety of important linkage variables are cleaned, and additional variables derived consistently. Conclusions & ImplicationsThe module has shown promising results at scale, successfully pre-processing datasets of over 91 million records. It will be a valuable tool for increasing the ease and efficiency of record linkage to the ONS’ population-spine. This will make linked data more accessible and increase the consistency of linked datasets, improving compatibility for onward linkages and the comparability of results. Future work will involve applying this cleaning method to a wider range of different datasets to further test the generalisability of the method, and increasing the adaptability of the module to allow for even greater variation in the input datasets.

How do cultural elements shape speak-up behavior beyond the patient safety context? An interprofessional perspective in an obstetrics and gynecology department.

Interprofessional working and learning thrives with speak-up behavior. Efforts to improve speak-up have mainly focused on isolated techniques and training programs within the patient safety scope, yet sustained improvement requires a cultural shift beyond this scope. This research investigates the influence of culture elements on speak-up behavior in interprofessional teams beyond the patient safety context. An exploratory qualitative study design was used in a Dutch hospital's Obstetrics and Gynecology department. A representative sample of stakeholders was purposefully selected, resulting in semi-structured interviews with 13 professionals from different professional backgrounds (nurses, midwifes, managers, medical specialists, and residents). A speak-up pledge was developed by the research team and used to prime participants for discussion. Data analysis involved three-step coding, which led to the development of themes. This study has identified six primary cultural themes that enhance speak-up behavior. These themes encompass the importance of managing a shared vision, the role of functional hierarchy, the significance of robust interpersonal relationships, the formulation of a strategy delineating when to speak up and when to exercise restraint, the promotion of an open-minded professional mindset, and the integration of cultural practices in the context of interprofessional working and learning. Six crucial cultural elements have been pinpointed to boost the practice of speaking up behavior in interprofessional working and learning. Remarkably, hierarchy should not be held responsible as the wrongdoer; instead, can be a great facilitator through respect and appreciation. We propose that employing transformational and humble leadership styles can provide guidance on effectively integrating the identified cultural elements into the workplace and provide an IMOI framework for effective interprofessional speak-up beyond patient safety.

The relationship between changes in functional connectivity gradients and cognitive-emotional disorders in sudden sensorineural hearing loss.

Sudden sensorineural hearing loss, a prevalent emergency in otolaryngology, is known to potentially precipitate cognitive and emotional disorders in affected individuals. Extensive research has documented the phenomenon of cortical functional reorganization in patients with sudden sensorineural hearing loss. However, the potential link between this neural functional remodelling and cognitive-emotional disorders remains unclear. To investigate this issue, 30 bilateral sudden sensorineural hearing loss patients and 30 healthy adults were recruited for this study. We collected clinical data and resting-state functional magnetic resonance imaging data from the participants. Gradient mapping analysis was employed to calculate the first three gradients for each subject. Subsequently, gradient changes in sudden sensorineural hearing loss patients were compared with healthy controls at global, regional and network levels. Finally, we explored the relationship between gradient values and clinical variables. The results revealed that at the global level, sudden sensorineural hearing loss did not exhibit significant differences in the primary gradient but showed a state of compression in the second and third gradients. At the regional level, sudden sensorineural hearing loss patients exhibited a significant reduction in the primary gradient values in the temporal pole and ventral prefrontal cortex, which were closely related to neuro-scale scores. Regarding the network level, sudden sensorineural hearing loss did not show significant differences in the primary gradient but instead displayed significant changes in the control network and default mode network in the second and third gradients. This study revealed disruptions in the functional hierarchy of sudden sensorineural hearing loss, and the alterations in functional connectivity gradients were closely associated with cognitive and emotional disturbances in patients. These findings provide new evidence for understanding the functional remodelling that occurs in sudden sensorineural hearing loss.

Different hierarchical reconfigurations in the brain by psilocybin and escitalopram for depression

Effective interventions for neuropsychiatric disorders may work by rebalancing the brain’s functional hierarchical organization. Here we directly investigated the effects of two different serotonergic pharmacological interventions on functional brain hierarchy in major depressive disorder in a two-arm double-blind phase II randomized controlled trial comparing psilocybin therapy (22 patients) with escitalopram (20 patients). Patients with major depressive disorder received either 2 × 25 mg of oral psilocybin, three weeks apart, plus six weeks of daily placebo (‘psilocybin arm’) or 2 × 1 mg of oral psilocybin, three weeks apart, plus six weeks of daily escitalopram (10–20 mg; ‘escitalopram arm’). Resting-state functional magnetic resonance imaging scans were acquired at baseline and three weeks after the second psilocybin dose (NCT03429075). The brain mechanisms were captured by generative effective connectivity, estimated from whole-brain modeling of resting state for each session and patient. Hierarchy was determined for each of these sessions using measures of directedness and trophic levels on the effective connectivity, which captures cycle structure, stability and percolation. The results showed that the two pharmacological interventions created significantly different hierarchical reconfigurations of whole-brain dynamics with differential, opposite statistical effect responses. Furthermore, the use of machine learning revealed significant differential reorganization of brain hierarchy before and after the two treatments. Machine learning was also able to predict treatment response with an accuracy of 0.85 ± 0.04. Overall, the results demonstrate that psilocybin and escitalopram work in different ways for rebalancing brain dynamics in depression. This suggests the hypothesis that neuropsychiatric disorders could be closely linked to the breakdown in regions orchestrating brain dynamics from the top of the hierarchy.

Decoding dynamic visual scenes across the brain hierarchy.

Understanding the computational mechanisms that underlie the encoding and decoding of environmental stimuli is a crucial investigation in neuroscience. Central to this pursuit is the exploration of how the brain represents visual information across its hierarchical architecture. A prominent challenge resides in discerning the neural underpinnings of the processing of dynamic natural visual scenes. Although considerable research efforts have been made to characterize individual components of the visual pathway, a systematic understanding of the distinctive neural coding associated with visual stimuli, as they traverse this hierarchical landscape, remains elusive. In this study, we leverage the comprehensive Allen Visual Coding-Neuropixels dataset and utilize the capabilities of deep learning neural network models to study neural coding in response to dynamic natural visual scenes across an expansive array of brain regions. Our study reveals that our decoding model adeptly deciphers visual scenes from neural spiking patterns exhibited within each distinct brain area. A compelling observation arises from the comparative analysis of decoding performances, which manifests as a notable encoding proficiency within the visual cortex and subcortical nuclei, in contrast to a relatively reduced encoding activity within hippocampal neurons. Strikingly, our results unveil a robust correlation between our decoding metrics and well-established anatomical and functional hierarchy indexes. These findings corroborate existing knowledge in visual coding related to artificial visual stimuli and illuminate the functional role of these deeper brain regions using dynamic stimuli. Consequently, our results suggest a novel perspective on the utility of decoding neural network models as a metric for quantifying the encoding quality of dynamic natural visual scenes represented by neural responses, thereby advancing our comprehension of visual coding within the complex hierarchy of the brain.

On the Lundgren hierarchy of helically symmetric turbulence

Abstract This paper analyzes the reduction of the infinite Lundgren-Monin-Novikov (LMN) hierarchy of probability density functions (PDFs) in the statistical theory of helically symmetric turbulence. Lundgren's hierarchy is considered a complete model, i.e. fully describes the joint multi-point statistic of turbulence though at the expense of dealing with an infinite set of integro-differential equations. The LMN hierarchy and its respective side-conditions are transformed to helical coordinates and thus are dimesionally reduced. In the course of development, a number of key questions were solved, namely in particular the transformation of PDFs and sample space velocities into orthonormal coordinate systems. In a validity check it is shown, that the mean momentum equations derived from the helical LMN hierarchy via statistical moment integration are identical to the mean momentum equations derived by direct ensemble averaging the Navier-Stokes equation, in helically symmetric form. Finally, we derive the equation for the characteristic function equivalent to the PDF equation in a helically symmetric frame, which allows to generate arbitrary n^th-order statistical moments by simple differentiation.

GNNGO3D: Protein Function Prediction Based on 3D Structure and Functional Hierarchy Learning

GNNGO3D: Protein Function Prediction Based on 3D Structure and Functional Hierarchy Learning

Hierarchy of exchange-correlation functionals in computing lattice thermal conductivities of rocksalt and zinc-blende semiconductors

Hierarchy of exchange-correlation functionals in computing lattice thermal conductivities of rocksalt and zinc-blende semiconductors

Hemispheric dominance in reading system alters contribution to face processing lateralization across development

Face processing dominates the right hemisphere. This lateralization can be affected by co-lateralization within the same system and influence between different systems, such as neural competition from reading acquisition. Yet, how the relationship pattern changes through development remains unknown. This study examined the lateralization of core face processing and word processing in different age groups. By comparing fMRI data from 36 school-aged children and 40 young adults, we investigated whether there are age and regional effects on lateralization, and how relationships between lateralization within and between systems change across development. Our results showed significant right hemispheric lateralization in the core face system and left hemispheric lateralization in reading-related areas for both age groups when viewing faces and texts passively. While all participants showed stronger lateralization in brain regions of higher functional hierarchy when viewing faces, only adults exhibited this lateralization when viewing texts. In both age cohorts, there was intra-system co-lateralization for face processing, whereas an inter-system relationship was only found in adults. Specifically, functional lateralization of Broca’s area during reading negatively predicted functional asymmetry in the FFA during face perception. This study initially provides neuroimaging evidence for the reading-induced neural competition theory from a maturational perspective in Chinese cohorts.

Morphological Brain Networks of White Matter: Mapping, Evaluation, Characterization, and Application.

Although white matter (WM) accounts for nearly half of adult brain, its wiring diagram is largely unknown. Here, an approach is developed to construct WM networks by estimating interregional morphological similarity based on structural magnetic resonance imaging. It is found that morphological WM networks showed nontrivial topology, presented good-to-excellent test-retest reliability, accounted for phenotypic interindividual differences in cognition, and are under genetic control. Through integration with multimodal and multiscale data, it is further showed that morphological WM networks are able to predict the patterns of hamodynamic coherence, metabolic synchronization, gene co-expression, and chemoarchitectonic covariance, and associated with structural connectivity. Moreover, the prediction followed WM functional connectomic hierarchy for the hamodynamic coherence, is related to genes enriched in the forebrain neuron development and differentiation for the gene co-expression, and is associated with serotonergic system-related receptors and transporters for the chemoarchitectonic covariance. Finally, applying this approach to multiple sclerosis and neuromyelitis optica spectrum disorders, it is found that both diseases exhibited morphological dysconnectivity, which are correlated with clinical variables of patients and are able to diagnose and differentiate the diseases. Altogether, these findings indicate that morphological WM networks provide a reliable and biologically meaningful means to explore WM architecture in health and disease.

Erroneous predictive coding across brain hierarchies in a non-human primate model of autism spectrum disorder

In autism spectrum disorder (ASD), atypical sensory experiences are often associated with irregularities in predictive coding, which proposes that the brain creates hierarchical sensory models via a bidirectional process of predictions and prediction errors. However, it remains unclear how these irregularities manifest across different functional hierarchies in the brain. To address this, we study a marmoset model of ASD induced by valproic acid (VPA) treatment. We record high-density electrocorticography (ECoG) during an auditory task with two layers of temporal control, and applied a quantitative model to quantify the integrity of predictive coding across two distinct hierarchies. Our results demonstrate a persistent pattern of sensory hypersensitivity and unstable predictions across two brain hierarchies in VPA-treated animals, and reveal the associated spatio-spectro-temporal neural signatures. Despite the regular occurrence of imprecise predictions in VPA-treated animals, we observe diverse configurations of underestimation or overestimation of sensory regularities within the hierarchies. Our results demonstrate the coexistence of the two primary Bayesian accounts of ASD: overly-precise sensory observations and weak prior beliefs, and offer a potential multi-layered biomarker for ASD, which could enhance our understanding of its diverse symptoms.