The Systems Biology Graphical Notation (SBGN) is an international community effort that aims to standardise the visualisation of pathways and networks for readers with diverse scientific backgrounds as well as to support an efficient and accurate exchange of biological knowledge between disparate research communities, industry, and other players in systems biology. SBGN comprises the three languages Entity Relationship, Activity Flow, and Process Description (PD) to cover biological and biochemical systems at distinct levels of detail. PD is closest to metabolic and regulatory pathways found in biological literature and textbooks. Its well-defined semantics offer a superior precision in expressing biological knowledge. PD represents mechanistic and temporal dependencies of biological interactions and transformations as a graph. Its different types of nodes include entity pools (e.g. metabolites, proteins, genes and complexes) and processes (e.g. reactions, associations and influences). The edges describe relationships between the nodes (e.g. consumption, production, stimulation and inhibition). This document details Level 1 Version 2.0 of the PD specification, including several improvements, in particular: 1) the addition of the equivalence operator, subunit, and annotation glyphs, 2) modification to the usage of submaps, and 3) updates to clarify the use of various glyphs (i.e. multimer, empty set, and state variable).
We propose a particle simulation method derived from a Hamiltonian with a mechanism of energy dissipation through particle-particle contact friction. The total energy dissipation variable S is paired with a function F(μ ) which works at each contact among constituent particles. A two dimensional quasi-static system is simulated to make clear the possibilities and the limitations of the proposed method. By changing the macroscopic dissipation coefficients γ systematically, we have shown how the macroscopic energy dissipation γ S and the microscopic contact dissipation F(μ ) is connected through the state of the granular system. In the compression/decompression cycle, pressure shows hysterisis but have an opposite trend depending whether it is in the direction of compression or not.
There has been a recent surge of interest within the field of quantum foundations regarding incorporating ideas from general relativity and quantum gravity. However, many quantum information tools remain agnostic to the underlying spacetime. For instance, whenever we draw a quantum circuit the effective spacetime imposed by the connectivity of the physical qubits which will realize this circuit is not taken into account. In this work, we aim to address this limitation by extending the framework of process theories to include a background spacetime structure. We introduce the notion of process implementations, i.e., decompositions of a process. A process is then embeddable if and only if one of its implementations can be embedded in such a way that all the component processes are localized and all wires follow timelike paths. While conceptually simple, checking for embeddability is generally computationally intractable. We therefore work towards simplifying this problem as much as possible, identifying a canonical subset of implementations that determine both the embeddability of a process and the causal structures distinguishable at least in some process theory. Notably, we discover countably infinite ''zigzag'' causal structures beyond those typically considered. While these can be ignored in classical theory, they seem to be essential in quantum theory, as the quantum CNOT gate can be implemented by all zigzag structures but not in a standard causal structure, except in the trivial undecomposed way. These zigzags could be significant for quantum causal modeling and the study of novel quantum resources.
Connectivity among isolated habitat patches via planktonic larval dispersal is crucial for maintaining the regional diversity of hydrothermal vents. Despite increasing sophistication of techniques for simulating dispersal, a lack of information on biological and behavioural traits of vent-associated species limits the applicability of these methods for inferring connectivity. Here we focus on the role of periodic reproduction on dispersal among hydrothermal vents, as periodic and seasonal spawning has increasingly been observed in a variety of taxa. For generalisability, we simulate the dispersal of larvae under treatments of monthly and continuous release timing at various depths, with consistent behavioural traits. Our results show a highly variable effect of periodicity on the characteristics and distribution of dispersal, which are heavily modified by the dispersal depth and source location. The capacity for reproductive periodicity to impact the among-site dispersal warrants further investigation into its prevalence and timing among vent-associated fauna.
Transformers have had a significant impact on natural language processing and have recently demonstrated their potential in computer vision. They have shown promising results over convolution neural networks in fundamental computer vision tasks. However, the scientific community has not fully grasped the inner workings of vision transformers, nor the basis for their decision-making, which underscores the importance of explainability methods. Understanding how these models arrive at their decisions not only improves their performance but also builds trust in AI systems. This study explores different explainability methods proposed for visual transformers and presents a taxonomy for organizing them according to their motivations, structures, and application scenarios. In addition, it provides a comprehensive review of evaluation criteria that can be used for comparing explanation results, as well as explainability tools and frameworks. Finally, the paper highlights essential but unexplored aspects that can enhance the explainability of visual transformers, and promising research directions are suggested for future investment.
