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Comparison of the Newtonian and relativistic predicted trajectories for a low-speed periodically delta-kicked system

Lan BL

Chaos, 2006 Sep;16(3):033107.
PMID: 17014212

The dynamics of a periodically delta-kicked Hamiltonian system moving at low speed (i.e., at speed much less than the speed of light) is studied numerically. In particular, the trajectory of the system predicted by Newtonian mechanics is compared with the trajectory predicted by special relativistic mechanics for the same parameters and initial conditions. We find that the Newtonian trajectory, although close to the relativistic trajectory for some time, eventually disagrees completely with the relativistic trajectory, regardless of the nature (chaotic, nonchaotic) of each trajectory. However, the agreement breaks down very fast if either the Newtonian or relativistic trajectory is chaotic, but very much slower if both the Newtonian and relativistic trajectories are nonchaotic. In the former chaotic case, the difference between the Newtonian and relativistic values for both position and momentum grows, on average, exponentially. In the latter nonchaotic case, the difference grows much slower, for example, linearly on average.
Newtonian and special-relativistic predictions for the trajectories of a low-speed scattering system

Lan BL, Borondo F

Phys Rev E Stat Nonlin Soft Matter Phys, 2011 Mar;83(3 Pt 2):036201.
PMID: 21517569

Newtonian and special-relativistic predictions, based on the same model parameters and initial conditions for the trajectory of a low-speed scattering system are compared. When the scattering is chaotic, the two predictions for the trajectory can rapidly diverge completely, not only quantitatively but also qualitatively, due to an exponentially growing separation taking place in the scattering region. In contrast, when the scattering is nonchaotic, the breakdown of agreement between predictions takes a very long time, since the difference between the predictions grows only linearly. More importantly, in the case of low-speed chaotic scattering, the rapid loss of agreement between the Newtonian and special-relativistic trajectory predictions implies that special-relativistic mechanics must be used, instead of the standard practice of using Newtonian mechanics, to correctly describe the scattering dynamics.
Fulltext Heavy-Tailed Fluctuations in the Spiking Output Intensity of Semiconductor Lasers with Optical Feedback

Lan BL, Masoller C

PLoS One, 2016;11(2):e0150027.
PMID: 26901346 DOI: 10.1371/journal.pone.0150027

Although heavy-tailed fluctuations are ubiquitous in complex systems, a good understanding of the mechanisms that generate them is still lacking. Optical complex systems are ideal candidates for investigating heavy-tailed fluctuations, as they allow recording large datasets under controllable experimental conditions. A dynamical regime that has attracted a lot of attention over the years is the so-called low-frequency fluctuations (LFFs) of semiconductor lasers with optical feedback. In this regime, the laser output intensity is characterized by abrupt and apparently random dropouts. The statistical analysis of the inter-dropout-intervals (IDIs) has provided many useful insights into the underlying dynamics. However, the presence of large temporal fluctuations in the IDI sequence has not yet been investigated. Here, by applying fluctuation analysis we show that the experimental distribution of IDI fluctuations is heavy-tailed, and specifically, is well-modeled by a non-Gaussian stable distribution. We find a good qualitative agreement with simulations of the Lang-Kobayashi model. Moreover, we uncover a transition from a less-heavy-tailed state at low pump current to a more-heavy-tailed state at higher pump current. Our results indicate that fluctuation analysis can be a useful tool for investigating the output signals of complex optical systems; it can be used for detecting underlying regime shifts, for model validation and parameter estimation.
Fulltext Statistical predictions for the dynamics of a low-speed system: Newtonian versus special-relativistic mechanics

Liang SN, Lan BL

PLoS One, 2012;7(5):e36430.
PMID: 22606259 DOI: 10.1371/journal.pone.0036430

The newtonian and special-relativistic statistical predictions for the mean, standard deviation and probability density function of the position and momentum are compared for the periodically-delta-kicked particle at low speed. Contrary to expectation, we find that the statistical predictions, which are calculated from the same parameters and initial gaussian ensemble of trajectories, do not always agree if the initial ensemble is sufficiently well-localized in phase space. Moreover, the breakdown of agreement is very fast if the trajectories in the ensemble are chaotic, but very slow if the trajectories in the ensemble are non-chaotic. The breakdown of agreement implies that special-relativistic mechanics must be used, instead of the standard practice of using newtonian mechanics, to correctly calculate the statistical predictions for the dynamics of a low-speed system.
Fulltext Comparison of newtonian and special-relativistic trajectories with the general-relativistic trajectory for a low-speed weak-gravity system

Liang SN, Lan BL

PLoS One, 2012;7(4):e34720.
PMID: 22536328 DOI: 10.1371/journal.pone.0034720

We show, contrary to expectation, that the trajectory predicted by general-relativistic mechanics for a low-speed weak-gravity system is not always well-approximated by the trajectories predicted by special-relativistic and newtonian mechanics for the same parameters and initial conditions. If the system is dissipative, the breakdown of agreement occurs for chaotic trajectories only. If the system is non-dissipative, the breakdown of agreement occurs for chaotic trajectories and non-chaotic trajectories. The agreement breaks down slowly for non-chaotic trajectories but rapidly for chaotic trajectories. When the predictions are different, general-relativistic mechanics must therefore be used, instead of special-relativistic mechanics (newtonian mechanics), to correctly study the dynamics of a weak-gravity system (a low-speed weak-gravity system).
Fulltext Comparison of computer-key-hold-time and alternating-finger-tapping tests for early-stage Parkinson's disease

Lan BL, Yeo JHW

PLoS One, 2019;14(6):e0219114.
PMID: 31247037 DOI: 10.1371/journal.pone.0219114

Giancardo et al. recently introduced the neuroQWERTY index (nQi), which is a novel motor index derived from computer-key-hold-time data using an ensemble regression algorithm, to detect early-stage Parkinson's disease. Here, we derive a much simpler motor index from their hold-time data, which is the standard deviation (SD) of the hold-time fluctuations, where fluctuation is defined as the difference between successive natural-log of hold time. Our results show the performance of the SD and nQi tests in discriminating early-stage subjects from controls do not differ, although the SD index is much simpler. There is also no difference in performance between the SD and alternating-finger-tapping tests.
Fulltext Newtonian versus special-relativistic statistical predictions for low-speed scattering

Liang SN, Borondo F, Lan BL

PLoS One, 2012;7(11):e48447.
PMID: 23152774 DOI: 10.1371/journal.pone.0048447

The statistical predictions of Newtonian and special-relativistic mechanics, which are calculated from an initially Gaussian ensemble of trajectories, are compared for a low-speed scattering system. The comparisons are focused on the mean dwell time, transmission and reflection coefficients, and the position and momentum means and standard deviations. We find that the statistical predictions of the two theories do not always agree as conventionally expected. The predictions are close if the scattering is non-chaotic but they are radically different if the scattering is chaotic and the initial ensemble is well localized in phase space. Our result indicates that for low-speed chaotic scattering, special-relativistic mechanics must be used, instead of the standard practice of using Newtonian mechanics, to obtain empirically-correct statistical predictions from an initially well-localized Gaussian ensemble.
Pulse-pair control of resonance leaking in molecular multiphoton transitions

Lan BL, Vrábel I, Jakubetz W

J Chem Phys, 2004 Dec 1;121(21):10401-10.
PMID: 15549920

We use model five-level systems to study resonance leaking of pi-pulse-induced multiphoton (MP) transitions along a strongly coupled anharmonic ladder. We demonstrate that the presence of a weakly bound background state attached to the ladder either in linear or Lambda configuration can have very pronounced effects on resonant MP ladder transitions, including essentially complete quenching of the primary transition. We also develop control strategies for the elimination of background state population based on phase-adjusted Gaussian pulse pairs and discuss the underlying control mechanisms. Finally we show that these strategies are effective in realistic molecular many-level systems. In particular, we demonstrate efficient pulse-pair control of resonance leaking in a 165-level system modeling vibrational excitation in HCN.
Abundance and generalisation in mutualistic networks: solving the chicken-and-egg dilemma

Fort H, Vázquez DP, Lan BL

Ecol Lett, 2016 Jan;19(1):4-11.
PMID: 26498731 DOI: 10.1111/ele.12535

A frequent observation in plant-animal mutualistic networks is that abundant species tend to be more generalised, interacting with a broader range of interaction partners than rare species. Uncovering the causal relationship between abundance and generalisation has been hindered by a chicken-and-egg dilemma: is generalisation a by-product of being abundant, or does high abundance result from generalisation? Here, we analyse a database of plant-pollinator and plant-seed disperser networks, and provide strong evidence that the causal link between abundance and generalisation is uni-directional. Specifically, species appear to be generalists because they are more abundant, but the converse, that is that species become more abundant because they are generalists, is not supported by our analysis. Furthermore, null model analyses suggest that abundant species interact with many other species simply because they are more likely to encounter potential interaction partners.
Flickering of cardiac state before the onset and termination of atrial fibrillation

Lan BL, Liew YW, Toda M, Kamsani SH

Chaos, 2020 May;30(5):053137.
PMID: 32491883 DOI: 10.1063/1.5130524

Complex dynamical systems can shift abruptly from a stable state to an alternative stable state at a tipping point. Before the critical transition, the system either slows down in its recovery rate or flickers between the basins of attraction of the alternative stable states. Whether the heart critically slows down or flickers before it transitions into and out of paroxysmal atrial fibrillation (PAF) is still an open question. To address this issue, we propose a novel definition of cardiac states based on beat-to-beat (RR) interval fluctuations derived from electrocardiogram data. Our results show the cardiac state flickers before PAF onset and termination. Prior to onset, flickering is due to a "tug-of-war" between the sinus node (the natural pacemaker) and atrial ectopic focus/foci (abnormal pacemakers), or the pacing by the latter interspersed among the pacing by the former. It may also be due to an abnormal autonomic modulation of the sinus node. This abnormal modulation may be the sole cause of flickering prior to termination since atrial ectopic beats are absent. Flickering of the cardiac state could potentially be used as part of an early warning or screening system for PAF and guide the development of new methods to prevent or terminate PAF. The method we have developed to define system states and use them to detect flickering can be adapted to study critical transition in other complex systems.
Fulltext Pneumocephalus and air travel: an experimental investigation on the effects of aircraft cabin pressure on intracranial pressure

Lim E, Lan BL, Ooi EH, Low HL

Sci Rep, 2020 08 12;10(1):13626.
PMID: 32788610 DOI: 10.1038/s41598-020-70614-w

This study investigates the effects of aircraft cabin pressure on intracranial pressure (ICP) elevation of a pneumocephalus patient. We propose an experimental setup that simulates the intracranial hydrodynamics of a pneumocephalus patient during flight. It consists of an acrylic box (skull), air-filled balloon [intracranial air (ICA)], water-filled balloon (cerebrospinal fluid and blood) and agarose gel (brain). The cabin was replicated using a custom-made pressure chamber. The setup can measure the rise in ICP during depressurization to levels similar to that inside the cabin at cruising altitude. ΔICP, i.e. the difference between mean cruising ICP and initial ICP, was found to increase with ICA volume and ROC. However, ΔICP was independent of the initial ICP. The largest ΔICP was 5 mmHg; obtained when ICA volume and ROC were 20 ml and 1,600 ft/min, respectively. The postulated ICA expansion and the subsequent increase in ICP in pneumocephalus patients during flight were successfully quantified in a laboratory setting. Based on the quantitative and qualitative analyses of the results, an ICA volume of 20 ml and initial ICP of 15 mmHg were recommended as conservative thresholds that are required for safe air travel among pneumocephalus patients. This study provides laboratory data that may be used by doctors to advise post-neurosurgical patients if they can safely fly.
CASPIANET++: A multidimensional Channel-Spatial Asymmetric attention network with Noisy Student Curriculum Learning paradigm for brain tumor segmentation

Liew A, Lee CC, Lan BL, Tan M

Comput Biol Med, 2021 09;136:104690.
PMID: 34352452 DOI: 10.1016/j.compbiomed.2021.104690

Convolutional neural networks (CNNs) have been used quite successfully for semantic segmentation of brain tumors. However, current CNNs and attention mechanisms are stochastic in nature and neglect the morphological indicators used by radiologists to manually annotate regions of interest. In this paper, we introduce a channel and spatial wise asymmetric attention (CASPIAN) by leveraging the inherent structure of tumors to detect regions of saliency. To demonstrate the efficacy of our proposed layer, we integrate this into a well-established convolutional neural network (CNN) architecture to achieve higher Dice scores, with less GPU resources. Also, we investigate the inclusion of auxiliary multiscale and multiplanar attention branches to increase the spatial context crucial in semantic segmentation tasks. The resulting architecture is the new CASPIANET++, which achieves Dice Scores of 91.19%, 87.6% and 81.03% for whole tumor, tumor core and enhancing tumor respectively. Furthermore, driven by the scarcity of brain tumor data, we investigate the Noisy Student method for segmentation tasks. Our new Noisy Student Curriculum Learning paradigm, which infuses noise incrementally to increase the complexity of the training images exposed to the network, further boosts the enhancing tumor region to 81.53%. Additional validation performed on the BraTS2020 data shows that the Noisy Student Curriculum Learning method works well without any additional training or finetuning.
Gradual Self-Training via Confidence and Volume Based Domain Adaptation for Multi Dataset Deep Learning-Based Brain Metastases Detection Using Nonlocal Networks on MRI Images

Liew A, Lee CC, Subramaniam V, Lan BL, Tan M

J Magn Reson Imaging, 2023 Jun;57(6):1728-1740.
PMID: 36208095 DOI: 10.1002/jmri.28456

BACKGROUND: Research suggests that treatment of multiple brain metastases (BMs) with stereotactic radiosurgery shows improvement when metastases are detected early, providing a case for BM detection capabilities on small lesions.
PURPOSE: To demonstrate automatic detection of BM on three MRI datasets using a deep learning-based approach. To improve the performance of the network is iteratively co-trained with datasets from different domains. A systematic approach is proposed to prevent catastrophic forgetting during co-training.
STUDY TYPE: Retrospective.
POPULATION: A total of 156 patients (105 ground truth and 51 pseudo labels) with 1502 BM (BrainMetShare); 121 patients with 722 BM (local); 400 patients with 447 primary gliomas (BrATS). Training/pseudo labels/validation data were distributed 84/51/21 (BrainMetShare). Training/validation data were split: 121/23 (local) and 375/25 (BrATS).
FIELD STRENGTH/SEQUENCE: A 5 T and 3 T/T1 spin-echo postcontrast (T1-gradient echo) (BrainMetShare), 3 T/T1 magnetization prepared rapid acquisition gradient echo postcontrast (T1-MPRAGE) (local), 0.5 T, 1 T, and 1.16 T/T1-weighted-fluid-attenuated inversion recovery (T1-FLAIR) (BrATS).
ASSESSMENT: The ground truth was manually segmented by two (BrainMetShare) and four (BrATS) radiologists and manually annotated by one (local) radiologist. Confidence and volume based domain adaptation (CAVEAT) method of co-training the three datasets on a 3D nonlocal convolutional neural network (CNN) architecture was implemented to detect BM.
STATISTICAL TESTS: The performance was evaluated using sensitivity and false positive rates per patient (FP/patient) and free receiver operating characteristic (FROC) analysis at seven predefined (1/8, 1/4, 1/2, 1, 2, 4, and 8) FPs per scan.
RESULTS: The sensitivity and FP/patient from a held-out set registered 0.811 at 2.952 FP/patient (BrainMetShare), 0.74 at 3.130 (local), and 0.723 at 2.240 (BrATS) using the CAVEAT approach with lesions as small as 1 mm being detected.
DATA CONCLUSION: Improved sensitivities at lower FP can be achieved by co-training datasets via the CAVEAT paradigm to address the problem of data sparsity.
LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.
Lung nodule classification using deep Local-Global networks

Al-Shabi M, Lan BL, Chan WY, Ng KH, Tan M

Int J Comput Assist Radiol Surg, 2019 Oct;14(10):1815-1819.
PMID: 31020576 DOI: 10.1007/s11548-019-01981-7

PURPOSE: Lung nodules have very diverse shapes and sizes, which makes classifying them as benign/malignant a challenging problem. In this paper, we propose a novel method to predict the malignancy of nodules that have the capability to analyze the shape and size of a nodule using a global feature extractor, as well as the density and structure of the nodule using a local feature extractor.
METHODS: We propose to use Residual Blocks with a 3 × 3 kernel size for local feature extraction and Non-Local Blocks to extract the global features. The Non-Local Block has the ability to extract global features without using a huge number of parameters. The key idea behind the Non-Local Block is to apply matrix multiplications between features on the same feature maps.
RESULTS: We trained and validated the proposed method on the LIDC-IDRI dataset which contains 1018 computed tomography scans. We followed a rigorous procedure for experimental setup, namely tenfold cross-validation, and ignored the nodules that had been annotated by
Community fluctuations and local extinction in a planktonic food web

Segura AM, Calliari D, Lan BL, Fort H, Widdicombe CE, Harmer R, et al.

Ecol Lett, 2017 04;20(4):471-476.
PMID: 28239940 DOI: 10.1111/ele.12749

Determining statistical patterns irrespective of interacting agents (i.e. macroecology) is useful to explore the mechanisms driving population fluctuations and extinctions in natural food webs. Here, we tested four predictions of a neutral model on the distribution of community fluctuations (CF) and the distributions of persistence times (APT). Novel predictions for the food web were generated by combining (1) body size-density scaling, (2) Taylor's law and (3) low efficiency of trophic transference. Predictions were evaluated on an exceptional data set of plankton with 15 years of weekly samples encompassing c. 250 planktonic species from three trophic levels, sampled in the western English Channel. Highly symmetric non-Gaussian distributions of CF support zero-sum dynamics. Variability in CF decreased while a change from an exponential to a power law distribution of APT from basal to upper trophic positions was detected. Results suggest a predictable but profound effect of trophic position on fluctuations and extinction in natural communities.
Fulltext The effects of mechanical noise bandwidth on balance across flat and compliant surfaces

Khor JZS, Gopalai AA, Lan BL, Gouwanda D, Ahmad SA

Sci Rep, 2021 06 10;11(1):12276.
PMID: 34112840 DOI: 10.1038/s41598-021-91422-w

Although the application of sub-sensory mechanical noise to the soles of the feet has been shown to enhance balance, there has been no study on how the bandwidth of the noise affects balance. Here, we report a single-blind randomized controlled study on the effects of a narrow and wide bandwidth mechanical noise on healthy young subjects' sway during quiet standing on firm and compliant surfaces. For the firm surface, there was no improvement in balance for both bandwidths-this may be because the young subjects could already balance near-optimally or optimally on the surface by themselves. For the compliant surface, balance improved with the introduction of wide but not narrow bandwidth noise, and balance is improved for wide compared to narrow bandwidth noise. This could be explained using a simple model, which suggests that adding noise to a sub-threshold pressure stimulus results in markedly different frequency of nerve impulse transmitted to the brain for the narrow and wide bandwidth noise-the frequency is negligible for the former but significantly higher for the latter. Our results suggest that if a person's standing balance is not optimal (for example, due to aging), it could be improved by applying a wide bandwidth noise to the feet.
Fulltext Optimised spectral effects of programmable LED arrays (PLA)s on bioelectricity generation from algal-biophotovoltaic devices

Ng FL, Phang SM, Lan BL, Kalavally V, Thong CH, Chong KT, et al.

Sci Rep, 2020 09 30;10(1):16105.
PMID: 32999346 DOI: 10.1038/s41598-020-72823-9

The biophotovoltaic cell (BPV) is deemed to be a potent green energy device as it demonstrates the generation of renewable energy from microalgae; however, inadequate electron generation from microalgae is a significant impediment for functional employment of these cells. The photosynthetic process is not only affected by the temperature, CO2 concentration and light intensity but also the spectrum of light. Thus, a detailed understanding of the influences of light spectrum is essential. Accordingly, we developed spectrally optimized light using programmable LED arrays (PLA)s to study the effect on algae growth and bioelectricity generation. Chlorella is a green microalga and contains chlorophyll-a (chl-a), which is the major light harvesting pigment that absorbs light in the blue and red spectrum. In this study, Chlorella is grown under a PLA which can optimally simulate the absorption spectrum of the pigments in Chlorella. This experiment investigated the growth, photosynthetic performance and bioelectricity generation of Chlorella when exposed to an optimally-tuned light spectrum. The algal BPV performed better under PLA with a peak power output of 0.581 mW m-2 for immobilized BPV device on day 8, which is an increase of 188% compared to operation under a conventional white LED light source. The photosynthetic performance, as measured using pulse amplitude modulation (PAM) fluorometry, showed that the optimized spectrum from the PLA gave an increase of 72% in the rETRmax value (190.5 μmol electrons m-2 s-1), compared with the conventional white light source. Highest algal biomass (1100 mg L-1) was achieved in the immobilized system on day eight, which translates to a carbon fixation of 550 mg carbon L-1. When artificial light is used for the BPV system, it should be optimized with the light spectrum and intensity best suited to the absorption capability of the pigments in the cells. Optimum artificial light source with algal BPV device can be integrated into a power management system for low power application (eg. environment sensor for indoor agriculture system).

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