Canonical Path Research Articles

Computing the minimal perimeter polygon for digital objects in the triangular tiling

This work presents an algorithm, together with its correctness proof, to determine the minimum perimeter polygon (MPP) for digital objects given as regular complexes in the triangular plane tiling. Such objects are edge-adjacency-connected sets of triangle tiles that have no end tiles, and the point set union of all their tiles forms a simple polygon. Nevertheless, the boundary paths of the objects are not assumed to be simple. Then the MPP is a weakly simple polygon that coincides with the relative convex hull (i.e., geodesic hull) of a set A with respect to a simple polygon B, where A⊂B, but A is not necessarily a polygon, in fact it is generally not connected. Our MPP algorithm relies on constructing and iteratively constraining cones of visibility through forthcoming boundary tiles, it uses the structure of the canonical boundary path, the MPP frontier is the shortest polygonal curve following this path. We also propose a boundary tracing algorithm to obtain such paths from the objects.

Adaptive Path Planning for Subsurface Plume Tracing with an Autonomous Underwater Vehicle

Autonomous underwater vehicles (AUVs) have been increasingly applied in marine environmental monitoring. Their outstanding capability of performing tasks without human intervention makes them a popular tool for environmental data collection, especially in unknown and remote regions. This paper addresses the path planning problem when AUVs are used to perform plume source tracing in an unknown environment. The goal of path planning is to locate the plume source efficiently. The path planning approach is developed using the Double Deep Q-Network (DDQN) algorithm in the deep reinforcement learning (DRL) framework. The AUV gains knowledge by interacting with the environment, and the optimal direction is extracted from the mapping obtained by a deep neural network. The proposed approach was tested by numerical simulation and on a real ground vehicle. In the numerical simulation, several initial sampling strategies were compared on the basis of survey efficiency. The results show that direct learning based on the interaction with the environment could be an appropriate survey strategy for plume source tracing problems. The comparison with the canonical lawnmower path used in practice showed that path planning using DRL algorithms could be potentially promising for large-scale environment exploration.

Historical State Formation Within and Beyond Europe

State formation is a critical concern for comparative politics. Much of the most influential literature has focused on the politically fragmented setting of early modern Europe, where warmaking fostered state consolidation and the development of institutions of representation and taxation. More recently, scholars have expanded this perspective by emphasizing the state-building implications of alternative forms of competition, interstate cooperation, and emulation, as well as the influence of a broader set of societal actors beyond belligerent rulers. The authors review recent scholarship on state formation that suggests that the canonical bellicist path is only one pathway to state consolidation, both in Europe and beyond. This article draws attention to the importance of geography and to new insights regarding the organization of state-society relations and the influence of regional and global economic engagements on state formation.

The structural power of reconfigurable circuits in the amoebot model

The amoebot model (Derakhshandeh et al. in: SPAA ACM, pp 220–222. https://doi.org/10.1145/2612669.2612712, 2014) has been proposed as a model for programmable matter consisting of tiny, robotic elements called amoebots. We consider the reconfigurable circuit extension (Feldmann et al. in J Comput Biol 29(4):317–343. https://doi.org/10.1089/cmb.2021.0363, 2022) of the geometric amoebot model that allows the amoebot structure to interconnect amoebots by so-called circuits. A circuit permits the instantaneous transmission of signals between the connected amoebots. In this paper, we examine the structural power of the reconfigurable circuits. We start with fundamental problems like the stripe computation problem where, given any connected amoebot structure S, an amoebot u in S, and some axis X, all amoebots belonging to axis X through u have to be identified. Second, we consider the global maximum problem, which identifies an amoebot at the highest possible position with respect to some direction in some given amoebot (sub)structure. A solution to this problem can be used to solve the skeleton problem, where a cycle of amoebots has to be found in the given amoebot structure which contains all boundary amoebots. A canonical solution to that problem can be used to come up with a canonical path, which provides a unique characterization of the shape of the given amoebot structure. Constructing canonical paths for different directions allows the amoebots to set up a spanning tree and to check symmetry properties of the given amoebot structure. The problems are important for a number of applications like rapid shape transformation, energy dissemination, and structural monitoring. Interestingly, the reconfigurable circuit extension allows polylogarithmic-time solutions to all of these problems.

Restorable Shortest Path Tiebreaking for Edge-Faulty Graphs

The restoration lemma by Afek et al. [ 3 ] proves that, in an undirected unweighted graph, any replacement shortest path avoiding a failing edge can be expressed as the concatenation of two original shortest paths. However, the lemma is tiebreaking-sensitive : if one selects a particular canonical shortest path for each node pair, it is no longer guaranteed that one can build replacement paths by concatenating two selected shortest paths. They left as an open problem whether a method of shortest path tiebreaking with this desirable property is generally possible. We settle this question affirmatively with the first general construction of restorable tiebreaking schemes . We then show applications to various problems in fault-tolerant network design. These include a faster algorithm for subset replacement paths, more efficient fault-tolerant (exact) distance labeling schemes, fault-tolerant subset distance preservers and + 4 additive spanners with improved sparsity, and fast distributed algorithms that construct these objects. For example, an almost immediate corollary of our restorable tiebreaking scheme is the first nontrivial distributed construction of sparse fault-tolerant distance preservers resilient to three faults.

Air pollution induces pyroptosis of human monocytes through activation of inflammasomes and Caspase-3-dependent pathways

According to the World Health Organization (WHO), air pollution is one of the most serious threats for our planet. Despite a growing public awareness of the harmful effects of air pollution on human health, the specific influence of particulate matter (PM) on human immune cells remains poorly understood. In this study, we investigated the effect of PM on peripheral blood monocytes in vitro. Monocytes from healthy donors (HD) were exposed to two types of PM: NIST (SRM 1648a, standard urban particulate matter from the US National Institute for Standards and Technology) and LAP (SRM 1648a with the organic fraction removed). The exposure to PM-induced mitochondrial ROS production followed by the decrease of mitochondrial membrane potential and activation of apoptotic protease activating factor 1 (Apaf-1), Caspase-9, and Caspase-3, leading to the cleavage of Gasdermin E (GSDME), and initiation of pyroptosis. Further analysis showed a simultaneous PM-dependent activation of inflammasomes, including NLRP3 (nucleotide-binding oligomerization domain-like receptor pyrin domain containing 3) and Caspase-1, followed by cleavage of Gasdermin D (GSDMD) and secretion of IL-1β. These observations suggest that PM-treated monocytes die by pyroptosis activated by two parallel signaling pathways, related to the inorganic and organic PM components. The release of IL-1β and expression of danger-associated molecular patterns (DAMPs) by pyroptotic cells further activated the remnant viable monocytes to produce inflammatory cytokines (TNF-α, IL-6, IL-8) and protected them from death induced by the second challenge with PM.In summary, our report shows that PM exposure significantly impacts monocyte function and induces their death by pyroptosis. Our observations indicate that the composition of PM plays a crucial role in this process—the inorganic fraction of PM is responsible for the induction of the Caspase-3-dependent pyroptotic pathway. At the same time, the canonical inflammasome path is activated by the organic components of PM, including LPS (Lipopolysaccharide/endotoxin).Graphical PM-induced pyroptosis of human monocytes. Particulate matter (PM) treatment affects monocytes viability already after 15 min of their exposure to NIST or LAP in vitro. The remnant viable monocytes in response to danger-associated molecular patterns (DAMPs) release pro-inflammatory cytokines and activate Th1 and Th17 cells. The mechanism of PM-induced cell death includes the increase of reactive oxygen species (ROS) production followed by collapse of mitochondrial membrane potential (ΔΨm), activation of Apaf-1, Caspase-9 and Caspase-3, leading to activation of Caspase-3-dependent pyroptotic pathway, where Caspase-3 cleaves Gasdermin E (GSDME) to produce a N-terminal fragment responsible for the switch from apoptosis to pyroptosis. At the same time, PM activates the canonical inflammasome pathway, where activated Caspase-1 cleaves the cytosolic Gasdermin D (GSDMD) to produce N-terminal domain allowing IL-1β secretion. As a result, PM-treated monocytes die by pyroptosis activated by two parallel pathways—Caspase-3-dependent pathway related to the inorganic fraction of PM and the canonical inflammasome pathway dependent on the organic components of PM.

Integrated Approach Including Docking, MD Simulations, and Network Analysis Highlights the Action Mechanism of the Cardiac hERG Activator RPR260243.

hERG is a voltage-gated potassium channel involved in the heart contraction whose defections are associated with the cardiac arrhythmia Long QT Syndrome type 2. The activator RPR260243 (RPR) represents a possible candidate to pharmacologically treat LQTS2 because it enhances the opening of the channel. However, the molecular detail of its action mechanism remains quite elusive. Here, we address the problem using a combination of docking, molecular dynamics simulations, and network analysis. We show that the drug preferably binds at the interface between the voltage sensor and the pore, enhancing the canonical activation path and determining a whole-structure rearrangement of the channel that slightly impairs inactivation.

Label-free learning of elliptic partial differential equation solvers with generalizability across boundary value problems

Traditional numerical discretization-based solvers of partial differential equations (PDEs) are fundamentally agnostic to domains, boundary conditions and coefficients. In contrast, machine learnt solvers have a limited generalizability across these elements of boundary value problems. This is strongly true in the case of surrogate models that are typically trained on direct numerical simulations of PDEs applied to one specific boundary value problem. In a departure from this direct approach, the label-free machine learning of solvers is centered on a loss function that does not use computed field solutions as labels. Instead, the PDE and boundary conditions are directly incorporated in residual form to express the loss function during training. However, their generalization across boundary conditions is limited and they remain strongly domain-dependent. Here, we present a framework that generalizes across domains, boundary conditions and coefficients while simultaneously learning the PDE in weak form. Our work explores the ability of convolutional neural network (CNN)-based encoder–decoder architectures to learn to solve a PDE in greater generality than its restriction to a particular boundary value problem. In this first Communication, we take the canonical path through elliptic PDEs and focus on steady-state diffusion, linear and nonlinear elasticity. Importantly, the learning happens independently of any labeled field data from either experiments or direct numerical solutions. We develop probabilistic CNNs in the Bayesian setting using variational inference. Extensive results for these problem classes demonstrate the framework’s ability to learn PDE solvers that generalize across hundreds of thousands of domains, boundary conditions and coefficients, including extrapolation beyond the learning regime. Once trained, the machine learning solvers are orders of magnitude faster than discretization-based solvers. They therefore have relevance to high-throughput solutions of PDEs on varied domains, boundary conditions and coefficients, such as for inverse modeling, optimization, design and decision-making. We place our work in the context of other recent machine learning solvers of PDEs including continuous operator learning frameworks, as well as make comparisons of performance where possible. Finally, we note extensions to transfer learning, active learning and reinforcement learning.

CTD: Cascaded Temporal Difference Learning for the Mean-Standard Deviation Shortest Path Problem

This paper investigates the reliable shortest path (RSP) planning problem from the reinforcement learning perspective. Different from canonical path planning methods, which require at least the first- order statistic (mean) and second-order statistic (variance) information of travel time distribution, we target at the RSP planning problem without the assumption of knowing any travel time distribution characteristic beforehand, and propose a cascaded temporal difference learning (CTD) method, which simultaneously estimates the mean and variance of the executing path and thereby gradually makes improvements through the generalized policy iteration (GPI) scheme, as the ego vehicle interacts with the environment. Extensive simulation results demonstrate the applicability of the proposed method for RSP learning in various transportation networks.

Surge: a fast open-source chemical graph generator

Chemical structure generators are used in cheminformatics to produce or enumerate virtual molecules based on a set of boundary conditions. The result can then be tested for properties of interest, such as adherence to measured data or for their suitability as drugs. The starting point can be a potentially fuzzy set of fragments or a molecular formula. In the latter case, the generator produces the set of constitutional isomers of the given input formula. Here we present the novel constitutional isomer generator surge based on the canonical generation path method. Surge uses the nauty package to compute automorphism groups of graphs. We outline the working principles of surge and present benchmarking results which show that surge is currently the fastest structure generator. Surge is available under a liberal open-source license.

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Overlapping prison/community tuberculosis outbreaks in Costa Rica revealed by alternative analysis of suboptimal material.

Costa Rica has a low incidence of tuberculosis. Thus, identifying transmission hotspots is key to implement interventions. A tuberculosis outbreak was suspected in a prison in Costa Rica. Given the suboptimal quality of the samples received in our laboratory in Madrid, we applied alternative schemes for their analysis. In the first scheme, we bypassed the standard approach of applying systematic mycobacterial interspersed repetitive units-variable number of tandem repeats (MIRU-VNTR) and used a strain-specific polymerase chain reaction (PCR) that allowed identifying a cluster involving six cases (C1). The second scheme followed the canonical MIRU-VNTR path coupled with a whole-genomic amplification step, by which a second unsuspected overlapping cluster (C2), was detected in the same prison. These findings justified the implementation of a surveillance programme adapted to local resources based on a tailored multiplex allele-specific oligonucleotide (ASO)-PCR targeting C1 and C2. Presence of the C2 strain at a different prison was determined. ASO-PCR was applied extensively and alerted to the active circulation of one of the strains within and beyond prisons. Our study shows that alternative methodological strategies may provide useful data in settings with lack of resources for performing systematic standard molecular epidemiology programmes and/or with suboptimal material for analysis.

Jellium at finite temperature using the restricted worm algorithm

We study the Jellium model of Wigner at finite, non-zero, temperature through a computer simulation using the canonical path integral worm algorithm where we successfully implemented the fixed-nodes free particles restriction necessary to circumvent the fermion sign problem. Our results show good agreement with the recent simulation data of Brown et al. and of other similar computer experiments on the Jellium model at high density and low temperature. Our algorithm can be used to treat any quantum fluid model of fermions at finite, non zero, temperature and has never been used before in literature.

The top 1%: quantifying the unequal distribution of malaria in Brazil

BackgroundAs malaria endemic countries strive towards elimination, intensified spatial heterogeneities of local transmission could undermine the effectiveness of traditional intervention policy.MethodsThe dynamic nature of large-scale and long-term malaria heterogeneity across Brazilian Amazon basin were explored by (1) exploratory analysis of Brazil’s rich clinical malaria reporting database from 2004 to 2018, and (2) adapting Gini coefficient to study the distribution of malaria cases in the region.ResultsAs transmission declined, heterogeneity increased with cases clustering into smaller subpopulations across the territory. In 2004, the 1% of health units with the greatest number of cases accounted for 46% of all reported Plasmodium vivax cases, whereas in 2018 52% of P. vivax cases occurred in the top 1% of health units. Plasmodium falciparum had lower levels of transmission than P. vivax, and also had greater levels of heterogeneity with 75% of cases occurring in the top 1% of health units. Age and gender stratification of cases revealed peri-domestic and occupational exposure settings that remained relatively stable.ConclusionThe pathway to decreasing incidence is characterized by higher proportions of cases in males, in adults, due to importation, and caused by P. vivax. Characterization of spatio-temporal heterogeneity and risk groups can aid stratification for improved malaria control towards elimination with increased heterogeneity potentially allowing for more efficient and cost-effective targeting. Although distinct epidemiological phenomena were clearly observed as malaria transmission declines, the authors argue that there is no canonical path to malaria elimination and a more targeted and dynamic surveillance will be needed if Brazil decides to adopt the elimination target.

URL normalization and assessing website structures in higher education websites

PurposeThis study aims to explore the character and attainment of an effective URL system by expanding the concept of URL normalization, originally connected to machine-reading access of web pages, to form a broader understanding of URL systematization that includes user-focused cognitive and practical elements.Design/methodology/approachA revised understanding of URL normalization will be used to critically analyzed URLs of main admissions pages from M1 universities, as designated by the Carnegie Foundation.FindingsThe study found that very few institutions implemented well-organized systems of Uniform Resource Locators (URLs) and redirects and that many included unintelligible and impractical URLs that would hinder the effective use of their websites.Practical implicationsA broader understanding of URL systematization will result in more effective website design. URLs must serve an indexical function pointing to a unique web resource, whatever the URL's format. However, URLs should also consider human usability issues and strive to be simple, short, communicable, intelligible and ultimately useful as part of social interactions. Poorly designed URLs create frustration, if not failure, by being difficult to use, confusing or interminable. An effective URL system should also include redirects to anticipate alternate, meaningful URLs that are different from the canonical path. The framework and recommendations arising from this study are applicable to many website structures.Originality/valueThe expanded understanding of the concept of URL normalization and subsequent evaluation principles can be used to assess the overall coherence and completeness of the website in general, thus improving website usability.

The relationship between the oxidative stress reaction and the microbial community by a combinative method of PA and CCA

Earthworms, as the first choice for soil monitoring and bio-remediation of cadmium (Cd)-contaminated soil, need to identify its mechanism under Cd stress. In this study, an artificial soil test method was used to determine the oxidative stress reaction indices, amino acid composition, and microbial community changes in earthworms under different stress durations and concentrations. For the first time, the canonical correlation analysis model and path analysis model were innovatively introduced into the data analysis to determine the mechanism that drives earthworm physiological functions after Cd stress. The results showed that in the low-stress concentration treatments (50–125 mg.kg−1 DW), there was a driving relationship between oxidative stress reaction and microbial community in earthworm, and the driving factor was glycyl-L-glutamic acid at 50 mg.kg−1 DW. With the increase of Cd stress intensity, the enzymes of oxidative stress promoted the survival microbes to begin to proliferate, and SOD became the main driving factor under 125 mg.kg−1 DW Cd stress. In the high-stress concentration treatments (250–500 mg.kg−1 DW), the driving effects were weakened or disappeared; while Cd-resistant microbial population appeared. This study provides a theoretical basis for the driving mechanism between oxidative stress effect and microbial community after Cd stress.

Biovalorization of the raw agro-industrial waste rice husk through directed production of xylanase by Thermomyces lanuginosus strain A3-1 DSM 105773: a statistical sequential model

The current work addresses a statistically optimized, economic, and efficient approach for the professional valorization of the massively accumulated non-efficiently utilized, raw agro-industrial waste rice husk (RH). Thermomyces lanuginosus strain A3-1 DSM 105773, a locally isolated strain, was employed for directed production of xylanase, an industrially important enzyme upon growing on RH-based medium. A three-step empirical sequential, statistical approach—one variable at a time (OVAT), Plackett–Burman design (PBD), and Box–Behnken design (BBD)—was employed to optimize the xylanase production through liquid-state fermentation of rice husk. Incubation temperature (50 °C) and ammonium sulfate concentration as an inorganic nitrogen source were the most appropriate parameters triggering the production of xylanase, deduced from OVAT. The three key determinants RH concentration, initial pH of the production medium, and incubation time did exhibit significant influences (P ≤ 0.001) on the production of xylanase, deduced from PBD. By the end of the optimization process, the optimal levels of RH, initial pH of the production medium, and incubation time were 3.8% (w/v), 4.5, and 8 days, respectively, with an agitation speed of 120 rpm to achieve a maximal xylanase level of 0.8344 U/mL with a fivefold enhancement deduced from the estimated ridge of the canonical path. Present data would reinforce the scaling up of xylanase production using the present powerful and reproducible approach for the simultaneous and proficient valorization of RH and production of xylanase for further exploitation in food, pharmaceutical, and textile industries.

The 2^k Neighborhoods for Grid Path Planning

Grid path planning is an important problem in AI. Its understanding has been key for the development of autonomous navigation systems. An interesting and rather surprising fact about the vast literature on this problem is that only a few neighborhoods have been used when evaluating these algorithms. Indeed, only the 4- and 8-neighborhoods are usually considered, and rarely the 16-neighborhood. This paper describes three contributions that enable the construction of effective grid path planners for extended 2k-neighborhoods; that is, neighborhoods that admit 2k neighbors per state, where k is a parameter. First, we provide a simple recursive definition of the 2k-neighborhood in terms of the 2k-1-neighborhood. Second, we derive distance functions, for any k ≥ 2, which allow us to propose admissible heuristics that are perfect for obstacle-free grids, which generalize the well-known Manhattan and Octile distances. Third, we define the notion of canonical path for the 2k-neighborhood; this allows us to incorporate our neighborhoods into two versions of A*, namely Canonical A* and Jump Point Search (JPS), whose performance, we show, scales well when increasing k. Our empirical evaluation shows that, when increasing k, the cost of the solution found improves substantially. Used with the 2k-neighborhood, Canonical A* and JPS, in many configurations, are also superior to the any-angle path planner Theta* both in terms of solution quality and runtime. Our planner is competitive with one implementation of the any-angle path planner, ANYA in some configurations. Our main practical conclusion is that standard, well-understood grid path planning technology may provide an effective approach to any-angle grid path planning.

Quasi‐shuffle algebras and renormalisation of rough differential equations

The objective of this work is to compare several approaches to the process of renormalisation in the context of rough differential equations using the substitution bialgebra on rooted trees known from backward error analysis of $B$-series. For this purpose, we present a so-called arborification of the Hoffman--Ihara theory of quasi-shuffle algebra automorphisms. The latter are induced by formal power series, which can be seen to be special cases of the cointeraction of two Hopf algebra structures on rooted forests. In particular, the arborification of Hoffman's exponential map, which defines a Hopf algebra isomorphism between the shuffle and quasi-shuffle Hopf algebra, leads to a canonical renormalisation that coincides with Marcus' canonical extension for semimartingale driving signals. This is contrasted with the canonical geometric rough path of Hairer and Kelly by means of a recursive formula defined in terms of the coaction of the substitution bialgebra.

Structural visualization of key steps in nucleosome reorganization by human FACT

Facilitates chromatin transcription (FACT) is a histone chaperone, which accomplishes both nucleosome assembly and disassembly. Our combined cryo-electron microscopy (EM) and native mass spectrometry (MS) studies revealed novel key steps of nucleosome reorganization conducted by a Mid domain and its adjacent acidic AID segment of human FACT. We determined three cryo-EM structures of respective octasomes complexed with the Mid-AID and AID regions, and a hexasome alone. We discovered extensive contacts between a FACT region and histones H2A, H2B, and H3, suggesting that FACT is competent to direct functional replacement of a nucleosomal DNA end by its phosphorylated AID segment (pAID). Mutational assays revealed that the aromatic and phosphorylated residues within pAID are essential for octasome binding. The EM structure of the hexasome, generated by the addition of Mid-pAID or pAID, indicated that the dissociation of H2A-H2B dimer causes significant alteration from the canonical path of the nucleosomal DNA.