BlenderPhotonics - a versatile environment for 3-D complex bio-tissue modeling and light transport simulations based on Blender

Significance: Rapid advances in biophotonics techniques require quantitative, model-based computational approaches to obtain functional and structural information from increasingly complex and multi-scaled anatomies. The lack of efficient tools to accurately model tissue structures and subsequently perform quantitative multi-physics modeling greatly impedes the clinical translation of these modalities. Aim: While the mesh-based Monte Carlo (MMC) method expands our capabilities in simulating complex tissues by using tetrahedral meshes, the generation of such domains often requires specialized meshing tools such as Iso2Mesh. Creating a simplified and intuitive interface for tissue anatomical modeling and optical simulations is essential towards making these advanced modeling techniques broadly accessible to the user community. Approach: We responded to the above challenge by combining the powerful, open-source 3-D modeling software, Blender, with state-of-the-art 3-D mesh generation and MC simulation tools, utilizing the interactive graphical user interface (GUI) in Blender as the front-end to allow users to create complex tissue mesh models, and subsequently launch MMC light simulations. Results: We have developed a Python-based Blender add-on -- BlenderPhotonics -- to interface with Iso2Mesh and MMC, allowing users to create, configure and refine complex simulation domains and run hardware-accelerated 3-D light simulations with only a few clicks. In this tutorial, we provide a comprehensive introduction to this new tool and walk readers through 5 examples, ranging from simple shapes to sophisticated realistic tissue models. Conclusion: BlenderPhotonics is user-friendly and open-source, leveraging the vastly rich ecosystem of Blender. It wraps advanced modeling capabilities within an easy-to-use and interactive interface. The latest software can be downloaded at http://mcx.space/bp.

Exogenous capture accounts for fundamental differences between prosaccade and antisaccade performance

Oculomotor circuits generate eye movements based on the physical salience of objects and current behavioral goals, exogenous and endogenous influences, respectively. However, the interactions between exogenous and endogenous mechanisms and their dynamic contributions to target selection have been difficult to resolve because they evolve extremely rapidly. In a recent study (Salinas et al., 2019), we achieved the necessary temporal precision using an urgent variant of the antisaccade task wherein motor plans are initiated early and choice accuracy depends sharply on when exactly the visual cue information becomes available. Empirical and modeling results indicated that the exogenous signal arrives ~80 ms after cue onset and rapidly accelerates the (incorrect) plan toward the cue, whereas the informed endogenous signal arrives ~25 ms later to favor the (correct) plan away from the cue. Here, we scrutinize a key mechanistic hypothesis about this dynamic, that the exogenous and endogenous signals act at different times and independently of each other. We test quantitative model predictions by comparing the performance of human participants instructed to look toward a visual cue versus away from it under high urgency. We find that, indeed, the exogenous response is largely impervious to task instructions; it simply flips its sign relative to the correct choice, and this largely explains the drastic differences in psychometric performance between the two tasks. Thus, saccadic choices are strongly dictated by the alignment between salience and behavioral goals.

Collaborative workflow between pathologists and deep learning for evaluation of tumor cellularity in lung adenocarcinoma

Owing to the high demand for molecular testing, the reporting of tumor cellularity in cancer samples has become a mandatory task for pathologists. However, the pathological estimation of tumor cellularity is often inaccurate. We developed a collaborative workflow between pathologists and artificial intelligence (AI) models to evaluate tumor cellularity in lung cancer samples and prospectively applied it to routine practice. We also developed a quantitative model that we validated and tested on retrospectively analyzed cases and ran the model prospectively in a collaborative workflow where pathologists could access the AI results and apply adjustments (Adjusted-Score). The Adjusted-Scores were validated by comparing them with the ground truth established by manual annotation of hematoxylin-eosin slides with reference to immunostains with thyroid transcription factor-1 and napsin A. For training, validation, retrospective testing, and prospective application of the model, we used 40, 10, 50, and 151 whole slide images, respectively. The sensitivity and specificity of tumor segmentation were 97% and 87%, and the accuracy of nuclei recognition was 99%. Pathologists altered the initial scores in 87% of the cases after referring to the AI results and found that the scores became more precise after collaborating with AI. For validation of Adjusted-Score, we found the Adjusted-Score was significantly closer to the ground truth than non-AI-aided estimates (p<0.05). Thus, an AI-based model was successfully implemented into the routine practice of pathological investigations. The proposed model for tumor cell counting efficiently supported the pathologists to improve the prediction of tumor cellularity for genetic tests.

Independent control of amplitude and period in a synthetic oscillator circuit with modified repressilator

Synthetic Biology aims to create predictable biological circuits and fully operational biological systems. Although there are methods to create more stable oscillators, such as repressilators, independently controlling the oscillation of reporter genes in terms of their amplitude and period is only on theoretical level. Here, we introduce a new oscillator circuit that can be independently controlled by two inducers in Escherichia coli. Some control components, including σECF11 and NahR, were added to the circuit. By systematically tuning the concentration of the inducers, salicylate and IPTG, the amplitude and period can be modulated independently. Furthermore, we constructed a quantitative model to forecast the regulation results. Under the guidance of the model, the expected oscillation can be regulated by choosing the proper concentration combinations of inducers. In summary, our work achieved independent control of the oscillator circuit, which allows the oscillator to be modularized and used in more complex circuit designs.

An atomistic model for the thermal resistance of a liquid–solid interface

The thermal resistance associated with the interface between a solid and a liquid is analysed from an atomistic point of view. Partial evaluation of the associated Green–Kubo integral elucidates the various factors governing heat transport across the interface and leads to a quantitative model for the thermal resistance in terms of atomistic-level system parameters. The model is validated using molecular dynamics simulations.

Experimental Evolution of Anticipatory Regulation in Escherichia coli

Environmental cues in an ecological niche are often temporal in nature. For instance, in temperate climates, temperature is higher in daytime compared to during night. In response to these temporal cues, bacteria have been known to exhibit anticipatory regulation, whereby triggering response to a yet to appear cue. Such an anticipatory response in known to enhance Darwinian fitness, and hence, is likely an important feature of regulatory networks in microorganisms. However, the conditions under which an anticipatory response evolves as an adaptive response are not known. In this work, we develop a quantitative model to study response of a population to two temporal environmental cues, and predict variables which are likely important for evolution of anticipatory regulatory response. We follow this with experimental evolution of Escherichia coli in alternating environments of rhamnose and paraquat for ∼850 generations. We demonstrate that growth in this cyclical environment leads to evolution of anticipatory regulation. As a result, pre-exposure to rhamnose leads to a greater fitness in paraquat environment. Genome sequencing reveals that this anticipatory regulation is encoded via mutations in global regulators. Overall, our study contributes to understanding of how environment shapes the topology of regulatory networks in an organism.

Megaripple mechanics: bimodal transport ingrained in bimodal sands

Aeolian sand transport is a major process shaping landscapes on Earth and on diverse celestial bodies. Conditions favoring bimodal sand transport, with fine-grain saltation driving coarse-grain reptation, give rise to the evolution of megaripples with a characteristic bimodal sand composition. Here, we derive a unified phase diagram for this special aeolian process and the ensuing nonequilibrium megaripple morphodynamics by means of a conceptually simple quantitative model, grounded in the grain-scale physics. We establish a well-preserved quantitative signature of bimodal aeolian transport in the otherwise highly variable grain size distributions, namely, the log-scale width (Krumbein phi scale) of their coarse-grain peaks. A comprehensive collection of terrestrial and extraterrestrial data, covering a wide range of geographical sources and environmental conditions, supports the accuracy and robustness of this unexpected theoretical finding. It could help to resolve ambiguities in the classification of terrestrial and extraterrestrial sedimentary bedforms.

Partial Results Regarding the Genetic Determinism of Growth Process in a Native Horse Breed using Wither’s Height

Study of variability in domestic animal populations is the foundation of quantitative genetics. Based on statistical methods, the weights of the total phenotype variation that belong to its different fractions (causal components) are quantified: variation due to gene additive effect, variation due to allelic and non-allelic interactions, variation due to environment (general and special), variation due to genotype-environment interaction and possibly variation due to the association between genotype and environment. In this study, during 2017-2020, we used the method of analysis of variance with two sources of variation. The material was represented by 538 individuals from Hucul horse breed analyzed at 18, 30 and 42 months old). The heritability of character was 0.3402±0.1546 (18 months), 0.5549±0.2225 (30 months), 0.4506±0.1895 (42 months), suggest that this is a hereditary condition that follows a quantitative model of inheritance, where the influence of additive genetic factors is moderate to intense. We can conclude that, in this native breed and for this character, a significant share of the phenotypic value is due to the additive effect of genes.