Granger causality using Jacobian in neural networks.

Granger causality is a commonly used method for uncovering information flow and dependencies in a time series. Here, we introduce JGC (Jacobian Granger causality), a neural network-based approach to Granger causality using the Jacobian as a measure of variable importance, and propose a variable selection procedure for inferring Granger causal variables with this measure, using criteria of significance and consistency. The resulting approach performs consistently well compared to other approaches in identifying Granger causal variables, the associated time lags, as well as interaction signs. In addition, we also discuss the need for contemporaneous variables in Granger causal modeling as well as how these neural network-based approaches reduce the impact of nonseparability in dynamical systems, a problem where predictive information on a target variable is not unique to its causes, but also contained in the history of the target variable itself.

Bali's Ancient Rice Terraces: A Hamiltonian Approach.

We propose a Hamiltonian approach to reproduce the relevant elements of the centuries-old Subak irrigation system in Bali, showing a cluster-size distribution of rice-field patches that is a power-law with an exponent of ∼2. Besides this exponent, the resulting system presents two equilibria. The first originates from a balance between energy and entropy contributions. The second arises from the specific energy contribution through a local Potts-type interaction in combination with a long-range antiferromagnetic interaction without attenuation. Finite-size scaling analysis shows that, as a result of the second equilibrium, the critical transition balancing energy and entropy contributions at the Potts (local ferromagnetic) regime is absorbed by the transition driven by the global-antiferromagnetic interactions, as the system size increases. The phase transition balancing energy and entropy contributions at the global-antiferromagnetic regime also shows signs of criticality. Our study extends the Hamiltonian framework to a new domain of coupled human-environmental interactions.

Chaotic semi-express buses in a loop.

Urban mobility involves many interacting components: buses, cars, commuters, pedestrians, trains, etc., making it a very complex system to study. Even a bus system responsible for delivering commuters from their origins to their destinations in a loop service already exhibits very complicated dynamics. Here, we investigate the dynamics of a simplified version of such a bus loop system consisting of two buses serving three bus stops. Specifically, we consider a configuration of one bus operating as a normal bus that picks up passengers from bus stops A and B and then delivers them to bus stop C, while the second bus acts as an express bus that picks up passengers only from bus stop B and then delivers them to bus stop C. The two buses are like asymmetric agents coupled to bus stop B as they interact via picking up passengers from this common bus stop. Intriguingly, this semi-express bus configuration is more efficient and has a lower average waiting time for buses compared to a configuration of two normal buses or a configuration of two express buses. We reckon that the efficiency arises from the chaotic dynamics exhibited in the semi-express system, where the tendency toward anti-bunching is greater than that toward bunching, in contradistinction to the regular bunching behavior of two normal buses or the independent periodic behavior of two non-interacting express buses.

Adaptive self-organization of Bali's ancient rice terraces.

Spatial patterning often occurs in ecosystems as a result of a self-organizing process caused by feedback between organisms and the physical environment. Here, we show that the spatial patterns observable in centuries-old Balinese rice terraces are also created by feedback between farmers' decisions and the ecology of the paddies, which triggers a transition from local to global-scale control of water shortages and rice pests. We propose an evolutionary game, based on local farmers' decisions that predicts specific power laws in spatial patterning that are also seen in a multispectral image analysis of Balinese rice terraces. The model shows how feedbacks between human decisions and ecosystem processes can evolve toward an optimal state in which total harvests are maximized and the system approaches Pareto optimality. It helps explain how multiscale cooperation from the community to the watershed scale could persist for centuries, and why the disruption of this self-organizing system by the Green Revolution caused chaos in irrigation and devastating losses from pests. The model shows that adaptation in a coupled human-natural system can trigger self-organized criticality (SOC). In previous exogenously driven SOC models, adaptation plays no role, and no optimization occurs. In contrast, adaptive SOC is a self-organizing process where local adaptations drive the system toward local and global optima.

Kinship structures create persistent channels for language transmission.

Languages are transmitted through channels created by kinship systems. Given sufficient time, these kinship channels can change the genetic and linguistic structure of populations. In traditional societies of eastern Indonesia, finely resolved cophylogenies of languages and genes reveal persistent movements between stable speech communities facilitated by kinship rules. When multiple languages are present in a region and postmarital residence rules encourage sustained directional movement between speech communities, then languages should be channeled along uniparental lines. We find strong evidence for this pattern in 982 individuals from 25 villages on two adjacent islands, where different kinship rules have been followed. Core groups of close relatives have stayed together for generations, while remaining in contact with, and marrying into, surrounding groups. Over time, these kinship systems shaped their gene and language phylogenies: Consistently following a postmarital residence rule turned social communities into speech communities.

Replica exchange molecular dynamics simulation of cross-fibrillation of IAPP and PrP106-126.

Aggregation of proteins into amyloid is the central hallmark of a number of protein diseases. Most studies were carried out on the aggregation between proteins of similar species. However, it was observed that some patients with certain protein disease can easily acquire another unrelated protein disease. As such, it is also important to examine aggregation between proteins of different species. Usually aggregation between proteins of the same species can be attributed to the similarity between their respective amino acid sequences. In this article, we were motivated by an experimental study of aggregation between amylin (Islet Amyloid Polypeptide, IAPP) and prion106-126 (PrP106-126) fragment (JACS, 2013, 135, 13582-9). It was found that the two non-homologous peptides can aggregate quickly to form fibrils in the presence of negatively charged lipid bilayer. We attempted to elucidate the molecular mechanism of the early stage of dimerization of these two peptides through extensive replica exchange molecular dynamics simulations. Conformations consisting of various degrees of ß-sheets structures, both intra-chain and inter-chain, were found in the simulations. The conformations of the aggregated complex are very diverse, which suggests that the cross-species fibrils formed between the two proteins are highly polymorphic. The driving forces are mainly hydrophobic interactions, including aromatic-aliphatic interactions. The palindromic region of PrP106-126 and SNNFGAIL region of IAPP were found to play important roles in the interaction. Our study sheds insight into the exciting research of protein cross-fibrillation. Proteins 2016; 84:1134-1146. © 2016 Wiley Periodicals, Inc.

Effect of geometry on the classical entanglement in a chaotic optical fiber.

The effect of boundary deformation on the classical entanglement which appears in the classical electromagnetic field is considered. A chaotic billiard geometry is used to explore the influence of the mechanical modification of the optical fiber cross-sectional geometry on the production of classical entanglement within the electromagnetic fields. For the experimental realization of our idea, we propose an optical fiber with a cross section that belongs to the family of Robnik chaotic billiards. Our results show that a modification of the fiber geometry from a regular to a chaotic regime can enhance the transverse mode classical entanglement.

Alzheimer's disease--a panorama glimpse.

The single-mutation of genes associated with Alzheimer's disease (AD) increases the production of Aß peptides. An elevated concentration of Aß peptides is prone to aggregation into oligomers and further deposition as plaque. Aß plaques and neurofibrillary tangles are two hallmarks of AD. In this review, we provide a broad overview of the diverses sources that could lead to AD, which include genetic origins, Aß peptides and tau protein. We shall discuss on tau protein and tau accumulation, which result in neurofibrillary tangles. We detail the mechanisms of Aß aggregation, fibril formation and its polymorphism. We then show the possible links between Aß and tau pathology. Furthermore, we summarize the structural data of Aß and its precursor protein obtained via Nuclear Magnetic Resonance (NMR) or X-ray crystallography. At the end, we go through the C-terminal and N-terminal truncated Aß variants. We wish to draw reader's attention to two predominant and toxic Aß species, namely Aß4-42 and pyroglutamate amyloid-beta peptides, which have been neglected for more than a decade and may be crucial in Aß pathogenesis due to their dominant presence in the AD brain.

Signs of criticality in social explosions.

The success of an on-line movement could be defined in terms of the shift to large-scale and the later off-line massive street actions of protests. The role of social media in this process is to facilitate the transformation from small or local feelings of disagreement into large-scale social actions. The way how social media achieves that effect is by growing clusters of people and groups with similar effervescent feelings, which otherwise would not be in touch with each other. It is natural to think that these kinds of macro social actions, as a consequence of the spontaneous and massive interactions, will attain the growth and divergence of those clusters, like the correlation length of statistical physics, giving rise to important simplifications on several statistics. In this work, we report the presence of signs of criticality in social demonstrations. Namely, similar power-law exponents are found whenever the distributions are calculated either considering time windows of the same length or with the same number of hashtag usages. In particular, the exponents for the distributions during the event were found to be smaller than before the event, and this is also observed either if we count the hashtags only once per user or if all their usages are considered. By means of network representations, we show that the systems present two kinds of high connectedness, characterised by either high or low values of modularity. The importance of analysing systems near a critical point is that any small disturbance can escalate and induce large-scale-nationwide-chain reactions.

Heme prevents amyloid beta peptide aggregation through hydrophobic interaction based on molecular dynamics simulation.

Heme, which is abundant in hemoglobin and many other hemoproteins, is known to play an important role in electron transfer, oxygen transport, regulation of gene expression, and many other biological functions. With the belief that the aggregation of Aß peptides forming higher order oligomers is one of the central pathological pathways in Alzheimer's disease, the formation of the Aß-heme complex is essential as it inhibits Aß aggregation and protects the neurons from degradation. In our studies, conventional molecular dynamics simulations were performed on the 1 Aß + 1 heme and 2 Aß + 4 hemes system, respectively, with the identification of several dominant binding motifs. We found that hydrophobic residues of the Aß peptide have a high affinity to interact with heme instead of the histidine residue. We conclude that hydrophobic interaction plays a dominant role in the Aß-heme complex formation which indirectly serves to physically prevent Aß aggregation.

Inferring origin-destination distribution of agent transfer in a complex network using deep gated recurrent units.

Predicting the origin-destination (OD) probability distribution of agent transfer is an important problem for managing complex systems. However, prediction accuracy of associated statistical estimators suffer from underdetermination. While specific techniques have been proposed to overcome this deficiency, there still lacks a general approach. Here, we propose a deep neural network framework with gated recurrent units (DNNGRU) to address this gap. Our DNNGRU is network-free, as it is trained by supervised learning with time-series data on the volume of agents passing through edges. We use it to investigate how network topologies affect OD prediction accuracy, where performance enhancement is observed to depend on the degree of overlap between paths taken by different ODs. By comparing against methods that give exact results, we demonstrate the near-optimal performance of our DNNGRU, which we found to consistently outperform existing methods and alternative neural network architectures, under diverse data generation scenarios.

Effects of correlation in an information ratchet with finite tape.

With the finite-tape autonomous information ratchet modeled by He et al. [Phys. Rev. E 105, 054131 (2022)2470-004510.1103/PhysRevE.105.054131], we recast the information processing second law, giving a tighter bound on the work extracted, in terms of the marginal bit-ratchet distribution defined from the joint tape-ratchet distribution. The marginal distribution is further utilized to probe and elucidate the conditions that lead to the presence of equilibrium and nonequilibrium stationary states in general, which are related to the effects of correlation. Applying our analysis to two designs of this information ratchet, where correlations within manifest differently, we uncover the mathematical condition for equilibrium stationary states for information ratchets that harness correlation, to identify them for engine operation during the transient phase.

The mechanism of antiparallel ß-sheet formation based on conditioned self-avoiding walk.

By introducing an additional hydrogen bond to hydrogen bond interaction in the force field of the CSAW (Conditioned Self-Avoiding Walk) model, we investigate into the mechanism of antiparallel ß-sheet formation based on the folding of a short polyalanine in gas phase. Through our numerical simulation, we detect the possible presence of a transient helix during ß-sheet formation, whose presence is shown to have slowed the formation of ß-sheets by an order of magnitude. While we observe the mechanisms of nucleation, zipping and induction that drives the formation of a ß-sheet, we uncover a new mechanism that involves transient ß-turns and short ß-sheets during the formation of long ß-sheets. Our results have enabled us to provide an overview on the mechanisms of ß-sheet formation via two main folding pathways: slow folding through the intermediate state of transient helix, and fast folding from the nucleation of ß-turn.

The toxicity of amyloid ß oligomers.

In this review, we elucidate the mechanisms of Aß oligomer toxicity which may contribute to Alzheimer's disease (AD). In particular, we discuss on the interaction of Aß oligomers with the membrane through the process of adsorption and insertion. Such interaction gives rises to phase transitions in the sub-structures of the Aß peptide from α-helical to ß-sheet structure. By means of a coarse-grained model, we exhibit the tendency of ß-sheet structures to aggregate, thus providing further insights to the process of membrane induced aggregation. We show that the aggregated oligomer causes membrane invagination, which is a precursor to the formation of pore structures and ion channels. Other pathological progressions to AD due to Aß oligomers are also covered, such as their interaction with the membrane receptors, and their direct versus indirect effects on oxidative stress and intraneuronal accumulation. We further illustrate that the molecule curcumin is a potential Aß toxicity inhibitor as a ß-sheet breaker by having a high propensity to interact with certain Aß residues without binding to them. The comprehensive understanding gained from these current researches on the various toxicity mechanisms show promises in the provision of better therapeutics and treatment strategies in the near future.

Information processing second law for an information ratchet with finite tape.

We model a class of discrete-time information ratchet with a finite tape and explore its thermodynamic consequence as a Maxwell demon. We found that, although it supports the operational regime of an engine or eraser, it cannot typically sustain these thermodynamic functionalities due to eventual equilibration as a result of the finite information capacity of the tape. Nonetheless, cumulative work can be accrued or expended through successive tape scans and we prove that at all time the ratchet obeys the information processing second law (IPSL). Unlike the IPSL for the infinite-tape ratchet which operates only at the stationary state, the IPSL here is applicable also at the transient phase of the ratchet operation. We explore two ratchet designs with the single-state perturbed coin (PC) ratchet being the simplest ratchet without memory, while the double-state modified Boyd's (MB) ratchet is the simplest ratchet with memory. Our analysis shows that the MB ratchet can harness correlation to accumulate more work by having a larger time constant to reach steady state relative to the PC ratchet.

Dynamics and potential significance of spontaneous activity in the habenula.

The habenula is an evolutionarily conserved structure of the vertebrate brain that is essential for behavioural flexibility and mood control. It is spontaneously active and is able to access diverse states when the animal is exposed to sensory stimuli. Here we investigate the dynamics of habenula spontaneous activity, to gain insight into how sensitivity is optimized. Two-photon calcium imaging was performed in resting zebrafish larvae at single cell resolution. An analysis of avalanches of inferred spikes suggests that the habenula is subcritical. Activity had low covariance and a small mean, arguing against dynamic criticality. A multiple regression estimator of autocorrelation time suggests that the habenula is neither fully asynchronous nor perfectly critical, but is reverberating. This pattern of dynamics may enable integration of information and high flexibility in the tuning of network properties, thus providing a potential mechanism for the optimal responses to a changing environment.Significance StatementSpontaneous activity in neurons shapes the response to stimuli. One structure with a high level of spontaneous neuronal activity is the habenula, a regulator of broadly acting neuromodulators involved in mood and learning. How does this activity influence habenula function? We show here that the habenula of a resting animal is near criticality, in a state termed reverberation. This pattern of dynamics is consistent with high sensitivity and flexibility, and may enable the habenula to respond optimally to a wide range of stimuli.

Behavioral structure of users in cryptocurrency market.

Human behavior as they engaged in financial activities is intimately connected to the observed market dynamics. Despite many existing theories and studies on the fundamental motivations of the behavior of humans in financial systems, there is still limited empirical deduction of the behavioral compositions of the financial agents from a detailed market analysis. Blockchain technology has provided an avenue for the latter investigation with its voluminous data and its transparency of financial transactions. It has enabled us to perform empirical inference on the behavioral patterns of users in the market, which we explore in the bitcoin and ethereum cryptocurrency markets. In our study, we first determine various properties of the bitcoin and ethereum users by a temporal complex network analysis. After which, we develop methodology by combining k-means clustering and Support Vector Machines to derive behavioral types of users in the two cryptocurrency markets. Interestingly, we found four distinct strategies that are common in both markets: optimists, pessimists, positive traders and negative traders. The composition of user behavior is remarkably different between the bitcoin and ethereum market during periods of local price fluctuations and large systemic events. We observe that bitcoin (ethereum) users tend to take a short-term (long-term) view of the market during the local events. For the large systemic events, ethereum (bitcoin) users are found to consistently display a greater sense of pessimism (optimism) towards the future of the market.

Impact of COVID-19 on Liver Transplantation in Hong Kong and Singapore: A Modelling Study.

BACKGROUND: Liver transplantation (LT) activities during the COVID-19 pandemic have been curtailed in many countries. The impact of various policies restricting LT on outcomes of potential LT candidates is unclear. METHODS: We studied all patients on the nationwide LT waitlists in Hong Kong and Singapore between January 2016 and May 2020. We used continuous time Markov chains to model the effects of different scenarios and varying durations of disruption on LT candidates. FINDINGS: With complete cessation of LT, the projected 1-year overall survival (OS) decreased by 3•6%, 10•51% and 19•21% for a 1-, 3- and 6-month disruption respectively versus no limitation to LT, while 2-year OS decreased by 4•1%, 12•55%, and 23•43% respectively. When only urgent (acute-on-chronic liver failure [ACLF] or acute liver failure) LT was allowed, the projected 1-year OS decreased by a similar proportion: 3•1%, 8•41% and 15•20% respectively. When deceased donor LT (DDLT) and urgent living donor LT (LDLT) were allowed, 1-year projected OS decreased by 1•2%, 5•1% and 8•85% for a 1-, 3- and 6-month disruption respectively. OS was similar when only DDLT was allowed. Complete cessation of LT activities for 3-months resulted in an increased projected incidence of ACLF and hepatocellular carcinoma (HCC) dropout at 1-year by 49•1% and 107•96% respectively. When only urgent LT was allowed, HCC dropout and ACLF incidence were comparable to the rates seen in the scenario of complete LT cessation. INTERPRETATION: A short and wide-ranging disruption to LT results in better outcomes compared with a longer duration of partial restrictions. FUNDING: None to disclose.

No-boarding buses: Synchronisation for efficiency.

We investigate a no-boarding policy in a system of N buses serving M bus stops in a loop, which is an entrainment mechanism to keep buses synchronised in a reasonably staggered configuration. Buses always allow alighting, but would disallow boarding if certain criteria are met. For an analytically tractable theory, buses move with the same natural speed (applicable to programmable self-driving buses), where the average waiting time experienced by passengers waiting at the bus stop for a bus to arrive can be calculated. The analytical results show that a no-boarding policy can dramatically reduce the average waiting time, as compared to the usual situation without the no-boarding policy. Subsequently, we carry out simulations to verify these theoretical analyses, also extending the simulations to typical human-driven buses with different natural speeds based on real data. Finally, a simple general adaptive algorithm is implemented to dynamically determine when to implement no-boarding in a simulation for a real university shuttle bus service.

Fitness preferential attachment as a driving mechanism in bitcoin transaction network.

Bitcoin is the earliest cryptocurrency and among the most successful ones to date. Recently, its dynamical evolution has attracted the attention of the research community due to its completeness and richness in historical records. In this paper, we focus on the detailed evolution of bitcoin trading with the aim of elucidating the mechanism that drives the formation of the bitcoin transaction network. Our empirical investigation reveals that although the temporal properties of the transaction network possesses scale-free degree distribution like many other networks, its formation mechanism is different from the commonly assumed models of degree preferential attachment or wealth preferential attachment. By defining the fitness value of each node as the ability of the node to attract new connections, we have instead uncovered that the observed scale-free degree distribution results from the intrinsic fitness of each node following a power-law distribution. Our finding thus suggests that the "good-get-richer" rather than the "rich-get-richer" paradigm operates within the bitcoin ecosystem. Based on these findings, we propose a model that captures the temporal generative process by means of a fitness preferential attachment and data-driven birth/death mechanism. Our proposed model is able to produce structural properties in good agreement with those obtained from the empirical bitcoin network.