Biological Networks Research Articles

SVMBN: A new method for predicting metabolite-disease associations based on biological networks and machine learning

Identifying metabolite-disease associations is of paramount significance. With the advancement of research, computational methods have surpassed traditional experiments in efficiency. Nevertheless, current computational methods often overlook the integration of multiomics data, and the performance of the predictive models used is limited. To address these limitations, we propose the SVMBN algorithm for predicting metabolite-disease associations. The proposed approach involves the following steps: First, six similarity calculation methods are employed to construct the metabolite similarity network and the disease similarity network separately. Second, the metabolite and disease similarity networks are combined to obtain the original link features. Third, nonnegative Matrix Factorization (NMF) is applied to extract effective features from the original features, thereby reducing noise. Finally, Support Vector Machine (SVM) is utilized to predict potential associations between metabolites and diseases. Experimental results demonstrate that the SVMBN algorithm achieves an average AUC of 0.98 in 5-fold cross-validation, indicating its superiority over other methods. Furthermore, case studies prove that the SVMBN algorithm can accurately forecast the relationships between metabolites and diseases.

Inbreeding in Chinese fir: Insights into the adaptive growth traits of selfed progeny from mRNA, miRNA, and copy number variation.

The impact of inbreeding on biological processes is well documented in individuals with severe inbreeding depression. However, the biological processes influencing the adaptive growth of normal selfed individuals are unknown. Here, we aimed to investigate how inbreeding affects gene expression for adaptive growth of normal selfed seedlings from a self-fertilizing parent in Chinese fir (Cunninghamia lanceolata). Using RNA-seq data from above- and underground tissues of abnormal and normal selfed seedlings, we analyzed GO biological processes network. We also sequenced small RNAs in the aboveground tissues and measured the copy number variations (CNV) of the hub genes. Phenotypic fitness analysis revealed that the normal seedlings were better adapted than their abnormal counterparts. Upregulated differentially expressed genes (DEGs) were associated with development processes, and downregulated DEGs were mainly enriched in fundamental metabolism and stress response. Results of mRNA-miRNA parallel sequencing revealed that upregulated target genes were predominantly associated with development, highlighting their crucial role in phosphorylation in signal transduction networks. We also discovered a moderate correlation (0.1328 < R2 < 0.6257) between CNV and gene expression levels for three hub genes (TMKL1, GT2, and RHY1A). We uncovered the key biological processes underpinning the growth of normal selfed seedlings and established the relationship between CNV and the expression levels of hub genes in selfed seedlings. Understanding the candidate genes involved in the growth of selfed seedlings will help us comprehend the genetic mechanisms behind inbreeding depression in the evolutionary biology of plants.

Kernel heterogeneity improves sparseness of natural images representations

Abstract Both biological and artificial neural networks inherently balance their performance with their operational cost, which characterizes their computational abilities. Typically, an efficient neuromorphic neural network is one that learns representations that reduce the redundancies and dimensionality of its input. For instance, in the case of sparse coding (SC), sparse representations derived from natural images yield representations that are heterogeneous, both in their sampling of input features and in the variance of those features. Here, we focused on this notion, and sought correlations between natural images’ structure, particularly oriented features, and their corresponding sparse codes. We show that representations of input features scattered across multiple levels of variance substantially improve the sparseness and resilience of sparse codes, at the cost of reconstruction performance. This echoes the structure of the model’s input, allowing to account for the heterogeneously aleatoric structures of natural images. We demonstrate that learning kernel from natural images produces heterogeneity by balancing between approximate and dense representations, which improves all reconstruction metrics. Using a parametrized control of the kernels’ heterogeneity of a convolutional SC algorithm, we show that heterogeneity emphasizes sparseness, while homogeneity improves representation granularity. In a broader context, this encoding strategy can serve as inputs to deep convolutional neural networks. We prove that such variance-encoded sparse image datasets enhance computational efficiency, emphasizing the benefits of kernel heterogeneity to leverage naturalistic and variant input structures and possible applications to improve the throughput of neuromorphic hardware.

Mondrian Abstraction and Language Model Embeddings for Differential Pathway Analysis.

In this study, we introduce the Mondrian Map, an innovative visualization tool inspired by Piet Mondrian's abstract art, to address the complexities inherent in visualizing biological networks. By converting intricate biological data into a structured and intuitive format, the Mondrian Map enables clear and meaningful representations of biological pathways, facilitating a deeper understanding of molecular dynamics. Each pathway is represented by a square whose size corresponds to fold change, with color indicating the direction of regulation (up or down) and statistical significance. The spatial arrangement of pathways is derived from language model embeddings, preserving neighborhood relationships and enabling the identification of clusters of related pathways. Additionally, colored lines highlight potential crosstalk between pathways, with distinctions between short- and long-range functional interactions. In a case study of glioblastoma multiforme (GBM), the Mondrian Map effectively revealed distinct pathway patterns across patient profiles at different stages of disease progression. These insights demonstrate the tool's potential to enhance downstream bioinformatics analysis by providing a more comprehensive and visually accessible overview of pathway interactions, offering new avenues for therapeutic exploration and personalized medicine.

Aquilaria crassna Extract Exerts Neuroprotective Effect against Benzo[a]pyrene-Induced Toxicity in Human SH-SY5Y Cells: An RNA-Seq-Based Transcriptome Analysis.

Benzo[a]pyrene (B[a]P) is known to inhibit neurodifferentiation and induce neurodegeneration. Agarwood or Aquilaria crassna (AC), a plant with health-promoting properties, may counteract the neurotoxic effects of B[a]P by promoting neuronal growth and survival. This study investigated the protective effect of AC leaf ethanolic extract (ACEE) on the B[a]P-induced impairment of neuronal differentiation. A transcriptomic analysis identified the canonical pathway, the biological network, and the differentially expressed genes (DEGs) that are changed in response to neuronal differentiation and neurogenesis. Several genes, including CXCR4, ENPP2, GAP43, GFRA2, NELL2, NFASC, NSG2, NGB, BASP1, and NEUROD1, in B[a]P-treated SH-SY5Y cells were up-regulated after treatment with ACEE. Notably, a Western blot analysis further confirmed that ACEE increased the protein levels of GAP43 and neuroglobin. B[a]P treatment led to decreased phosphorylation of Akt and increased phosphorylation of ERK in SH-SY5Y cells; however, ACEE was able to reverse these effects. Clionasterol and lupenone were identified in ACEE. Molecular docking showed that these two phytochemicals had significant interactions with CXCR4, GDNF family receptor alpha (GFRA), and retinoid X receptors (RXRs). In conclusion, ACEE may be a potential alternative medicine for the prevention of impaired neuronal differentiation and neurodegenerative diseases.

A novel hierarchical network-based approach to unveil the complexity of functional microbial genome

Biological networks serve a crucial role in elucidating intricate biological processes. While interspecies environmental interactions have been extensively studied, the exploration of gene interactions within species, particularly among individual microorganisms, is less developed. The increasing amount of microbiome genomic data necessitates a more nuanced analysis of microbial genome structures and functions. In this context, we introduce a complex structure using higher-order network theory, “Solid Motif Structures (SMS)”, via a hierarchical biological network analysis of genomes within the same genus, effectively linking microbial genome structure with its function. Leveraging 162 high-quality genomes of Microcystis, a key freshwater cyanobacterium within microbial ecosystems, we established a genome structure network. Employing deep learning techniques, such as adaptive graph encoder, we uncovered 27 critical functional subnetworks and their associated SMSs. Incorporating metagenomic data from seven geographically distinct lakes, we conducted an investigation into Microcystis’ functional stability under varying environmental conditions, unveiling unique functional interaction models for each lake. Our work compiles these insights into an extensive resource repository, providing novel perspectives on the functional dynamics within Microcystis. This research offers a hierarchical network analysis framework for understanding interactions between microbial genome structures and functions within the same genus.

Single neuromorphic memristor closely emulates multiple synaptic mechanisms for energy efficient neural networks

Biological neural networks do not only include long-term memory and weight multiplication capabilities, as commonly assumed in artificial neural networks, but also more complex functions such as short-term memory, short-term plasticity, and meta-plasticity - all collocated within each synapse. Here, we demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation. They can act as multi-functional hardware synapses in a class of bio-inspired deep neural networks (DNN) that make use of both long- and short-term synaptic dynamics and are capable of meta-learning or learning-to-learn. The resulting bio-inspired DNN is then trained to play the video game Atari Pong, a complex reinforcement learning task in a dynamic environment. Our analysis shows that the energy consumption of the DNN with multi-functional memristive synapses decreases by about two orders of magnitude as compared to a pure GPU implementation. Based on this finding, we infer that memristive devices with a better emulation of the synaptic functionalities do not only broaden the applicability of neuromorphic computing, but could also improve the performance and energy costs of certain artificial intelligence applications.

A practically efficient algorithm for identifying critical control proteins in directed probabilistic biological networks

Network controllability is unifying the traditional control theory with the structural network information rooted in many large-scale biological systems of interest, from intracellular networks in molecular biology to brain neuronal networks. In controllability approaches, the set of minimum driver nodes is not unique, and critical nodes are the most important control elements because they appear in all possible solution sets. On the other hand, a common but largely unexplored feature in network control approaches is the probabilistic failure of edges or the uncertainty in the determination of interactions between molecules. This is particularly true when directed probabilistic interactions are considered. Until now, no efficient algorithm existed to determine critical nodes in probabilistic directed networks. Here we present a probabilistic control model based on a minimum dominating set framework that integrates the probabilistic nature of directed edges between molecules and determines the critical control nodes that drive the entire network functionality. The proposed algorithm, combined with the developed mathematical tools, offers practical efficiency in determining critical control nodes in large probabilistic networks. The method is then applied to the human intracellular signal transduction network revealing that critical control nodes are associated with important biological features and perturbed sets of genes in human diseases, including SARS-CoV-2 target proteins and rare disorders. We believe that the proposed methodology can be useful to investigate multiple biological systems in which directed edges are probabilistic in nature, both in natural systems or when determined with large uncertainties in-silico.

Deep Learning-Driven Optimization Strategies for Teaching Decisions in Smart Classrooms

With the rapid advancement of information technology, smart classrooms have increasingly become a vital component of modern education. By integrating technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data, smart classrooms provide a smart, efficient educational setting for teachers and students. However, the challenge of fully utilizing these technologies to enhance teaching effectiveness in smart classrooms remains unresolved. Existing research has highlighted the significant potential of deep learning in optimizing teaching decisions. However, its application faces challenges, including insufficient integration of technologies and limited effectiveness in practical implementations. The main focus of this study encompasses three parts: firstly, a biological neural network model targeted at optimizing teaching decisions, which emulates the mechanisms of biological neural networks to efficiently optimize teaching decisions; secondly, an active consciousness teaching decision model within smart classroom settings, which merges deep learning with theories of active consciousness to support dynamic, intelligent teaching decisions; and thirdly, a biologically inspired teaching process coordination network in smart classrooms, designed to optimize and coordinate educational processes based on biological principles. Through these investigations, this study provides both theoretical and practical support for optimizing teaching decisions in smart classrooms, offering significant academic value and practical application prospects.

3D chromatin-based variant-to-gene maps across 57 human cell types reveal the cellular and genetic architecture of autoimmune disease susceptibility.

A portion of the genetic basis for many common autoimmune disorders has been uncovered by genome-wide association studies (GWAS), but GWAS do not reveal causal variants, effector genes, or the cell types impacted by disease-associated variation. We have generated 3D genomic datasets consisting of promoter-focused Capture-C, Hi-C, ATAC-seq, and RNA-seq and integrated these data with GWAS of 16 autoimmune traits to physically map disease-associated variants to the effector genes they likely regulate in 57 human cell types. These 3D maps of gene cis-regulatory architecture are highly powered to identify the cell types most likely impacted by disease-associated genetic variation compared to 1D genomic features, and tend to implicate different effector genes than eQTL approaches in the same cell types. Most of the variants implicated by these cis-regulatory architectures are highly trait-specific, but nearly half of the target genes connected to these variants are shared across multiple autoimmune disorders in multiple cell types, suggesting a high level of genetic diversity and complexity among autoimmune diseases that nonetheless converge at the level of target gene and cell type. Substantial effector gene sharing led to the common enrichment of similar biological networks across disease and cell types. However, trait-specific pathways representing potential areas for disease-specific intervention were identified. To test this, we pharmacologically validated squalene synthase, a cholesterol biosynthetic enzyme encoded by the FDFT1 gene implicated by our approach in MS and SLE, as a novel immunomodulatory drug target controlling inflammatory cytokine production by human T cells. These data represent a comprehensive resource for basic discovery of gene cis-regulatory mechanisms, and the analyses reported reveal mechanisms by which autoimmune-associated variants act to regulate gene expression, function, and pathology across multiple, distinct tissues and cell types.

BEROLECMI: a novel prediction method to infer circRNA-miRNA interaction from the role definition of molecular attributes and biological networks

Circular RNA (CircRNA)–microRNA (miRNA) interaction (CMI) is an important model for the regulation of biological processes by non-coding RNA (ncRNA), which provides a new perspective for the study of human complex diseases. However, the existing CMI prediction models mainly rely on the nearest neighbor structure in the biological network, ignoring the molecular network topology, so it is difficult to improve the prediction performance. In this paper, we proposed a new CMI prediction method, BEROLECMI, which uses molecular sequence attributes, molecular self-similarity, and biological network topology to define the specific role feature representation for molecules to infer the new CMI. BEROLECMI effectively makes up for the lack of network topology in the CMI prediction model and achieves the highest prediction performance in three commonly used data sets. In the case study, 14 of the 15 pairs of unknown CMIs were correctly predicted.

DNA Methylation of PXDN Is Associated with Early-Life Adversity in Adult Mental Disorders.

Early-life adversity (ELA) is characterized by exposure to traumatic events during early periods of life, particularly involving emotional, sexual and/or physical adversities during childhood. Mental disorders are strongly influenced by environmental and lifestyle-related risk factors including ELA. However, the molecular link between ELA and the risk of an adult mental disorder is still not fully understood. Evidence is emerging that long-lasting changes in the epigenetic processes regulating gene expression, such as DNA methylation, play an important role in the biological mechanisms linking ELA and mental disorders. Based on a recent study, we analyzed the DNA methylation of a specific CpG site within the gene PXDN-cg10888111-in blood in the context of ELA across a set of psychiatric disorders, namely Borderline Personality Disorder (BPD), Major Depressive Disorder (MDD) and Social Anxiety Disorder (SAD), and its potential contribution to their pathogenesis. We found significant hypermethylation in mentally ill patients with high levels of ELA compared to patients with low levels of ELA, whereas cg10888111 methylation in healthy control individuals was not affected by ELA. Further investigations revealed that this effect was driven by the MDD cohort. Providing a direct comparison of cg10888111 DNA methylation in blood in the context of ELA across three mental disorders, our results indicate the role of PXDN regulation in the response to ELA in the pathogenesis of mental disorders, especially MDD. Further studies will be needed to validate these results and decipher the corresponding biological network that is involved in the transmission of ELA to an adult mental disorder in general.

A Comparative Study of Ambipolar Material in Conventional and Access Region-Based Devices

Neuromorphic devices utilizing atomically thin 2D materials show promise for large-scale computing by emulating biological neural networks. Floating gate transistors (FGTs) typically fall into two categories based on the selection of the semiconductor channel layer material. One category involves unipolar semiconductors that transmit only one type of majority carrier, while the second category consists of ambipolar semiconductors, tunable to switch the type of majority carrier. This flexibility enables devices to switch modes and respond to specific stimulation, such as molybdenum ditelluride (MoTe2), proven to be valid as a channel material [1]. Here, MoTe2, hexagonal boron nitride (h-BN), and graphene (Gr) were applied to construct the ambipolar FGT (AFGT). Additionally, access regions (ARs) were introduced in the AFGT to fabricate AR-based AFGT (AR-AFGT), altering the transmission mechanism of carriers in the channel layer compared with the AFGT device [2].2D van der Waal materials were stacked in the order of Gr/h-BN/MoTe2 to fabricate AFGTs with source/drain electrodes overlapping the Gr, serving as the floating gate (FG), with a channel length of about 2 µm. Gr/h-BN/MoTe2 AR-AFGTs were fabricated by placing ARs, which do not overlap with FG, in proximity to source/drain electrodes with a channel length of 10 µm. We measured and compared the transfer characteristic of AFGT and AR-AFGT devices. The AFGT exhibited obvious ambipolar behavior with dominant n-branch and p-branch. However, in the case of AR-AFGT, a significant n-branch portion was observed. These phenomena may arise from the AR increasing the distance from the electrode to the storage layer, indicating that MoTe2 acts as a unipolar material. Our study compared AFGT and AR-AFGT devices, highlighting their unique traits. While AFGT exhibited significant ambipolar behavior, AR-AFGT, influenced by the increased distance, displayed a distinct unipolar profile with an emphasis on the n-branch. The integration of AR stands out as a key factor for creating adaptable neuromorphic devices. References Wu, E., et al., Tunable and nonvolatile multibit data storage memory based on MoTe2/boron nitride/graphene heterostructures through contact engineering. Nanotechnology, 2020. 31(48): p. 485205. Sasaki, T., et al., Material and Device Structure Designs for 2D Memory Devices Based on the Floating Gate Voltage Trajectory. ACS Nano, 2021. 15(4): p. 6658-6668. Figure 1

(Invited) Enabling Bio-Realistic Artificial Intelligence Hardware with Neuromorphic Nanoelectronics

The exponentially improving performance of digital computers has recently slowed due to the speed and power consumption issues resulting from the von Neumann bottleneck. In contrast, neuromorphic computing aims to circumvent these limitations by spatially co-locating logic and memory in a manner analogous to biological neuronal networks [1]. Beyond reducing power consumption, neuromorphic devices provide efficient architectures for image recognition, machine learning, and artificial intelligence [2]. This talk will explore how low-dimensional nanoelectronic materials enable gate-tunable neuromorphic devices [3]. For example, by utilizing self-aligned, atomically thin heterojunctions, dual-gated Gaussian transistors have been realized, which show tunable anti-ambipolarity for artificial neurons, competitive learning, spiking circuits, and mixed-kernel support vector machines [4,5]. In addition, field-driven defect motion in polycrystalline monolayer MoS2 enables gate-tunable memristive phenomena that serve as the basis of hybrid memristor/transistor devices (i.e., ‘memtransistors’) that concurrently provide logic and data storage functions [6]. The planar geometry of memtransistors further allows multiple contacts and dual gating that mimic the behavior of biological systems such as heterosynaptic responses [7]. Moreover, control over polycrystalline grain structure enhances the tunability of potentiation and depression, which enables unsupervised continuous learning in spiking neural networks [8]. Finally, the moiré potential in asymmetric twisted bilayer graphene/hexagonal boron nitride heterostructures gives rise to robust electronic ratchet states. The resulting hysteretic, non-volatile injection of charge carriers enables room-temperature operation of moiré synaptic transistors with diverse bio-realistic neuromorphic functionalities and efficient compute-in-memory designs for low-power artificial intelligence and machine learning hardware [9].[1] V. K. Sangwan, et al., Matter, 5, 4133 (2022).[2] V. K. Sangwan, et al., Nature Nanotechnology, 15, 517 (2020).[3] M. E. Beck, et al., ACS Nano, 14, 6498 (2020).[4] M. E. Beck, et al., Nature Communications, 11, 1565 (2020).[5] X. Yan, et al., Nature Electronics, DOI: 10.1038/s41928-023-01042-7 (2023).[6] X. Yan, et al., Advanced Materials, 34, 2108025 (2022).[7] H.-S. Lee, et al., Advanced Functional Materials, 30, 2003683 (2020).[8] J. Yuan, et al., Nano Letters, 21, 6432 (2021).[9] X. Yan, et al., Nature, DOI: 10.1038/s41586-023-06791-1 (2023).

Output Stabilizing Control of Complex Biological Networks Based on Boolean Algebra Analysis.

Output stabilizing control of biological systems is of utmost importance in systems biology since key phenotypes of biological networks are often encoded by a small subset of their phenotypic marker nodes. This study addresses the challenge of output stabilizing control for complex biological systems modeled by Boolean networks (BNs). The objective is to identify a set of constant control inputs capable of driving the BN toward a desirable long-term behavior with respect to specified output nodes. Leveraging the algebraic properties of Boolean logic, we develop a novel control algorithm that reformulates the output stabilizing control problem into a simple graph theoretic problem involving auxiliary BNs, the scale of which significantly decreases compared to the original BN. The proposed method ensures superiority over previous results in terms of both the number of control inputs and computational loads, since it searches for the solution within the reduced BNs while retaining essential structures needed for output stabilization. The efficacy of the proposed control scheme is demonstrated through extensive numerical experiments with complex random BNs and real biological networks. To support the reproducible research initiative, detailed results of numerical experiments are provided in the supplementary material, and all the implementation codes are made accessible at https://github.com/choonlog/OutputStabilization.

Mathematical analysis and asymptotic predictions of chemical-driven swimming living organisms in weighted networks

This paper derives well-posedness and asymptotic results that provide qualitative information about the behavior, mechanism and strategies used by living organisms to navigate their biological networks. Chemical driven swimming is a captivating phenomenon that is observed in various living organisms like bacteria and protozoa but the problem in weighted networks is more complex, since the equations of parabolic-parabolic Keller-Segel model coupled with incompressible Navier-Stokes equations must be reformulated in a discrete setting. The starting point is to transpose the coupled system from the Euclidean case to the connected networks with certain network-theoretic simplified approaches, which yields fruitful key results. These results not only enable the construction of global solutions but also serve as a foundation for determining information about the stability and large time behavior of the system. Then, decay rates are well established to predict important features, such as how quickly weak solutions at a given point decrease over time due to dissipative processes. Additionally, the L1- convergence of cell densities towards the self-similar Gaussian solution of the heat equation is well proved by time dependent scaling, which shows that the solution maintains its shape and only scales in time and space as time evolves. Finally, this paper includes many numerical tests through a recent robust numerical scheme to illustrate the theoretical results and to develop computational control and prediction of living organisms' trajectories around central nodes in networked flows.

Real-time hardware emulation of neural cultures: A comparative study of in vitro, in silico and in duris silico models

Biological neural networks are well known for their capacity to process information with extremely low power consumption. Fields such as Artificial Intelligence, with high computational costs, are seeking for alternatives inspired in biological systems. An inspiring alternative is to implement hardware architectures that replicate the behavior of biological neurons but with the flexibility in programming capabilities of an electronic device, all combined with a relatively low operational cost. To advance in this quest, here we analyze the capacity of the HEENS hardware architecture to operate in a similar manner as an in vitro neuronal network grown in the laboratory. For that, we considered data of spontaneous activity in living neuronal cultures of about 400 neurons and compared their collective dynamics and functional behavior with those obtained from direct numerical simulations (in silico) and hardware implementations (in duris silico). The results show that HEENS is capable to mimic both the in vitro and in silico systems with high efficient-cost ratio, and on different network topological designs. Our work shows that compact low-cost hardware implementations are feasible, opening new avenues for future, highly efficient neuromorphic devices and advanced human–machine interfacing.

Optimal Inference of Hidden Markov Models Through Expert-Acquired Data.

This paper focuses on inferring a general class of hidden Markov models (HMMs) using data acquired from experts. Expert-acquired data contain decisions/actions made by humans/users for various objectives, such as navigation data reflecting drivers' behavior, cybersecurity data carrying defender decisions, and biological data containing the biologist's actions (e.g., interventions and experiments). Conventional inference methods rely on temporal changes in data without accounting for expert knowledge. This paper incorporates expert knowledge into the inference of HMMs by modeling expert behavior as an imperfect reinforcement learning agent. The proposed method optimally quantifies experts' perceptions about the system model, which, alongside the temporal changes in data, contributes to the inference process. The proposed inference method is derived through a combination of dynamic programming and optimal recursive Bayesian estimation. The applicability of this method is demonstrated to a wide range of inference criteria, such as maximum likelihood and maximum a posteriori. The performance of the proposed method is investigated through a comprehensive numerical experiment using a benchmark problem and biological networks.

Unravelling the mechanics of knitted fabrics through hierarchical geometric representation

Knitting interloops one-dimensional yarns into three-dimensional fabrics that exhibit behaviour beyond their constitutive materials. How extensibility and anisotropy emerge from the hierarchical organization of yarns into knitted fabrics has long been unresolved. We seek to unravel the mechanical roles of tensile mechanics, assembly and dynamics arising from the yarn level on fabric nonlinearity by developing a yarn-based dynamical model. This physically validated model captures the mechanical response of knitted fabrics, analogous to flexible metamaterials and biological fibre networks due to geometric nonlinearity within such hierarchical systems. Fabric anisotropy originates from observed yarn–yarn rearrangements during alignment dynamics and is topology-dependent. This yarn-based model also provides a design space of knitted fabrics to embed functionalities by varying geometric configuration and material property in instructed procedures compatible to machine manufacturing. Our hierarchical approach to build up a knitted fabric computationally modernizes an ancient craft and represents a first step towards mechanical programmability of knitted fabrics in wide engineering applications.

Self-organization toward 1/f noise in deep neural networks.

In biological neural networks, it has been well recognized that a healthy brain exhibits 1/f noise patterns. However, in artificial neural networks that are increasingly matching or even out-performing human cognition, this phenomenon has yet to be established. In this work, we found that similar to that of their biological counterparts, 1/f noise exists in artificial neural networks when trained on time series classification tasks. Additionally, we found that the activations of the neurons are the closest to 1/f noise when the neurons are highly utilized. Conversely, if the network is too large and many neurons are underutilized, the neuron activations deviate from 1/f noise patterns toward that of white noise.