Growth, collapse, and self-organized criticality in complex networks

To understand how certain dynamical behaviors can or cannot persist as the underlying network grows is a problem of increasing importance in complex dynamical systems as well as sustainability science and engineering. We address the question of whether a complex network of nonlinear oscillators can maintain its synchronization stability as it expands or grows. A network in the real world can never be completely synchronized due to noise and/or external disturbances. This is especially the case when, mathematically, the transient synchronous state during the growth process becomes marginally stable, as a local perturbation can trigger a rapid deviation of the system from the vicinity of the synchronous state. In terms of the nodal dynamics, a large scale avalanche over the entire network can be triggered in the sense that the individual nodal dynamics diverge from the synchronous state in a cascading manner within a short time period. Because of the high dimensionality of the networked system, the transient process for the system to recover to the synchronous state can be extremely long. Introducing a tolerance threshold to identify the desynchronized nodes, we find that, after an initial stage of linear growth, the network typically evolves into a critical state where the addition of a single new node can cause a group of nodes to lose synchronization, leading to synchronization collapse for the entire network. A statistical analysis indicates that, the distribution of the size of the collapse is approximately algebraic (power law), regardless of the fluctuations in the system parameters. This is indication of the emergence of self-organized criticality. We demonstrate the generality of the phenomenon of synchronization collapse using a variety of complex network models, and uncover the underlying dynamical mechanism through an eigenvector analysis.Comment: 10pages, 6 figure

The validity and reliability of an automated method of scoring dental arch relationships in unilateral cleft lip and palate using the modified Huddart-Bodenham scoring system

Objective: To evaluate an automated software tool for the assessment of dental arch relationships using the modified Huddart and Bodenham index.Design: Cohort of 43 models of subjects aged 9-21 with UCLP and the ten GOSLON reference models sets.Method: The 53 sets of plaster models were scored using the MHB index and scanned (R700, 3Shape, Copenhagen, Denmark). The digital models were MHB scored visually (Orthoanalyzer, 3Shape, Copenhagen, Denmark) and landmarked for automatic scoring using a Rhino software plug-in (Rhinoceros, version 5, www.rhino3d.co.uk). Scoring/landmarking was undertaken by three observers and repeated after one month. Intra- and inter-observer reproducibility were tested using Cronbach’s Alpha and intraclass correlation coefficients (ICC) (threshold > 0.9). Bland-Altman plots demonstrated inter-observer agreement for each model format. Random and systematic error with digital landmark identification error were determined using the x, y and z co-ordinates for 28 models digitized twice one month apart using Cronbach’s alpha and a t-test, respectively.Results: Intra-operator landmark identification was excellent (Cronbach’s alpha = 0.933) with no differences between sessions (P>0.05). Intra-observer reproducibility was excellent for all examiners (Cronbach’s alpha and ICC 0.986-0.988). Inter-observer reproducibility was highest for the software plug-in (0.991), followed by plaster (0.989) and Orthoanalyzer (0.979) and Bland-Altman plots confirmed no systematic bias and greater consistency of scores with the automated software.Conclusion: The automated MHB software tool is valid, reproducible and the most objective method of assessing maxillary arch constriction for patients with UCLP

Near-Infrared Super Resolution Imaging with Metallic Nanoshell Particle Chain Array

We propose a near-infrared super resolution imaging system without a lens or a mirror but with an array of metallic nanoshell particle chain. The imaging array can plasmonically transfer the near-field components of dipole sources in the incoherent and coherent manners and the super resolution images can be reconstructed in the output plane. By tunning the parameters of the metallic nanoshell particle, the plasmon resonance band of the isolate nanoshell particle red-shifts to the near-infrared region. The near-infrared super resolution images are obtained subsequently. We calculate the field intensity distribution at the different planes of imaging process using the finite element method and find that the array has super resolution imaging capability at near-infrared wavelengths. We also show that the image formation highly depends on the coherence of the dipole sources and the image-array distance.Comment: 15 pages, 6 figure

Changes in deep neck muscle length from the neutral to forward head posture. A cadaveric study using Thiel cadavers

Forward head posture (FHP) is one of the most common postural deviations. Deep neck muscle imbalance of individuals with FHP is of primary concern in clinical rehabilitation. However, there is scarce quantitative research on changes in deep neck muscle length with the head moving forward. This study aimed to investigate changes in deep neck muscle length with different severity levels of FHP. Six Thiel‐embalmed cadavers (four males and two females) were dissected, and 16 deep neck muscles in each cadaver were modeled by a MicroScribe 3D Digitizer in the neutral head posture, slight FHP, and severe FHP. The craniovertebral angle was used to evaluate the degrees of FHP. Quantitative length change of the deep neck muscles was analyzed using Rhinoceros 3D. In slight FHP significant changes in length occurred in four muscles: two shortened (upper semispinalis capitis, rectus capitis posterior minor) and two lengthened (longus capitis, splenius cervicis). In severe FHP all occipital extensors were significantly shortened (10.6 ± 6.4%), except for obliquus capitis superior, and all cervical extensors were significantly lengthened (4.8 ± 3.4%), while longus capitis (occipital flexor) and the superior oblique part of the longus colli (cervical flexor) were lengthened by 8.8 ± 3.8% and 4.2 ± 3.1%, respectively. No significant length change was observed for the axial rotator. This study presents an alternate anatomical insight into the clinical rehabilitation of FHP. Six muscles appear to be important in restoring optimal head posture, with improvements in FHP being related to interventions associated with the occipital and cervical extensors

Improved Cryo-EM Pose Estimation and 3D Classification through Latent-Space Disentanglement

Due to the extremely low signal-to-noise ratio (SNR) and unknown poses (projection angles and image shifts) in cryo-electron microscopy (cryo-EM) experiments, reconstructing 3D volumes from 2D images is very challenging. In addition to these challenges, heterogeneous cryo-EM reconstruction requires conformational classification. In popular cryo-EM reconstruction algorithms, poses and conformation classification labels must be predicted for every input cryo-EM image, which can be computationally costly for large datasets. An emerging class of methods adopted the amortized inference approach. In these methods, only a subset of the input dataset is needed to train neural networks for the estimation of poses and conformations. Once trained, these neural networks can make pose/conformation predictions and 3D reconstructions at low cost for the entire dataset during inference. Unfortunately, when facing heterogeneous reconstruction tasks, it is hard for current amortized-inference-based methods to effectively estimate the conformational distribution and poses from entangled latent variables. Here, we propose a self-supervised variational autoencoder architecture called "HetACUMN" based on amortized inference. We employed an auxiliary conditional pose prediction task by inverting the order of encoder-decoder to explicitly enforce the disentanglement of conformation and pose predictions. Results on simulated datasets show that HetACUMN generated more accurate conformational classifications than other amortized or non-amortized methods. Furthermore, we show that HetACUMN is capable of performing heterogeneous 3D reconstructions of a real experimental dataset.Comment: 21 page