Using genetic comparisons of populations from Arizona, Mexico, and Texas to investigate fall armyworm migration in the American southwest.

Fall armyworm (FAW) is a global agricultural pest, causing substantial economic losses in corn and many other crops. Complicating efforts to control this pest is its capacity for long distance flights, which has been described in greatest detail for the central and eastern sections of the United States. FAW infestations are also routinely found in agricultural areas in southern Arizona, which lie beyond the western limits of the mapped migratory pathways. Climate suitability analysis found that the affected Arizona locations cannot support permanent FAW populations, indicating that these FAW most likely arise from annual migrations. A better understanding of this migration would provide insights into how large moth populations can move across desert habitats as well as the degree of gene flow occurring between FAW populations across the North American continent. In this study the Arizona populations were genetically characterized and compared to a selection of permanent and migratory FAW from multiple sites in the United States and Mexico. The results are consistent with migratory contributions from permanent populations in the states of Texas (United States) and Sinaloa (Mexico), while also providing evidence of significant barriers to gene flow between populations within Mexico. An unexpected finding was that two genetically distinct FAW subpopulations known as "host strains" have a differential distribution in the southwest that may indicate significant differences in their migration behavior in this region. These findings indicate that the combination of mitochondrial and Z-linked markers have advantages in comparing FAW populations that can complement and extend the findings from other methods.

Single-cell profiling and zebrafish avatars reveal LGALS1 as immunomodulating target in glioblastoma.

Glioblastoma (GBM) remains the most malignant primary brain tumor, with a median survival rarely exceeding 2 years. Tumor heterogeneity and an immunosuppressive microenvironment are key factors contributing to the poor response rates of current therapeutic approaches. GBM-associated macrophages (GAMs) often exhibit immunosuppressive features that promote tumor progression. However, their dynamic interactions with GBM tumor cells remain poorly understood. Here, we used patient-derived GBM stem cell cultures and combined single-cell RNA sequencing of GAM-GBM co-cultures and real-time in vivo monitoring of GAM-GBM interactions in orthotopic zebrafish xenograft models to provide insight into the cellular, molecular, and spatial heterogeneity. Our analyses revealed substantial heterogeneity across GBM patients in GBM-induced GAM polarization and the ability to attract and activate GAMs-features that correlated with patient survival. Differential gene expression analysis, immunohistochemistry on original tumor samples, and knock-out experiments in zebrafish subsequently identified LGALS1 as a primary regulator of immunosuppression. Overall, our work highlights that GAM-GBM interactions can be studied in a clinically relevant way using co-cultures and avatar models, while offering new opportunities to identify promising immune-modulating targets.

Machine learning-based ozone and PM2.5 forecasting: Application to multiple AQS sites in the Pacific Northwest.

Air quality in the Pacific Northwest (PNW) of the U.S has generally been good in recent years, but unhealthy events were observed due to wildfires in summer or wood burning in winter. The current air quality forecasting system, which uses chemical transport models (CTMs), has had difficulty forecasting these unhealthy air quality events in the PNW. We developed a machine learning (ML) based forecasting system, which consists of two components, ML1 (random forecast classifiers and multiple linear regression models) and ML2 (two-phase random forest regression model). Our previous study showed that the ML system provides reliable forecasts of O3 at a single monitoring site in Kennewick, WA. In this paper, we expand the ML forecasting system to predict both O3 in the wildfire season and PM2.5 in wildfire and cold seasons at all available monitoring sites in the PNW during 2017-2020, and evaluate our ML forecasts against the existing operational CTM-based forecasts. For O3, both ML1 and ML2 are used to achieve the best forecasts, which was the case in our previous study: ML2 performs better overall (R2 = 0.79), especially for low-O3 events, while ML1 correctly captures more high-O3 events. Compared to the CTM-based forecast, our O3 ML forecasts reduce the normalized mean bias (NMB) from 7.6 to 2.6% and normalized mean error (NME) from 18 to 12% when evaluating against the observation. For PM2.5, ML2 performs the best and thus is used for the final forecasts. Compared to the CTM-based PM2.5, ML2 clearly improves PM2.5 forecasts for both wildfire season (May to September) and cold season (November to February): ML2 reduces NMB (-27 to 7.9% for wildfire season; 3.4 to 2.2% for cold season) and NME (59 to 41% for wildfires season; 67 to 28% for cold season) significantly and captures more high-PM2.5 events correctly. Our ML air quality forecast system requires fewer computing resources and fewer input datasets, yet it provides more reliable forecasts than (if not, comparable to) the CTM-based forecast. It demonstrates that our ML system is a low-cost, reliable air quality forecasting system that can support regional/local air quality management.

RS-FISH: precise, interactive, fast, and scalable FISH spot detection.

Fluorescent in-situ hybridization (FISH)-based methods extract spatially resolved genetic and epigenetic information from biological samples by detecting fluorescent spots in microscopy images, an often challenging task. We present Radial Symmetry-FISH (RS-FISH), an accurate, fast, and user-friendly software for spot detection in two- and three-dimensional images. RS-FISH offers interactive parameter tuning and readily scales to large datasets and image volumes of cleared or expanded samples using distributed processing on workstations, clusters, or the cloud. RS-FISH maintains high detection accuracy and low localization error across a wide range of signal-to-noise ratios, a key feature for single-molecule FISH, spatial transcriptomics, or spatial genomics applications.

PyZebrascope: An Open-Source Platform for Brain-Wide Neural Activity Imaging in Zebrafish.

Understanding how neurons interact across the brain to control animal behaviors is one of the central goals in neuroscience. Recent developments in fluorescent microscopy and genetically-encoded calcium indicators led to the establishment of whole-brain imaging methods in zebrafish, which record neural activity across a brain-wide volume with single-cell resolution. Pioneering studies of whole-brain imaging used custom light-sheet microscopes, and their operation relied on commercially developed and maintained software not available globally. Hence it has been challenging to disseminate and develop the technology in the research community. Here, we present PyZebrascope, an open-source Python platform designed for neural activity imaging in zebrafish using light-sheet microscopy. PyZebrascope has intuitive user interfaces and supports essential features for whole-brain imaging, such as two orthogonal excitation beams and eye damage prevention. Its camera module can handle image data throughput of up to 800 MB/s from camera acquisition to file writing while maintaining stable CPU and memory usage. Its modular architecture allows the inclusion of advanced algorithms for microscope control and image processing. As a proof of concept, we implemented a novel automatic algorithm for maximizing the image resolution in the brain by precisely aligning the excitation beams to the image focal plane. PyZebrascope enables whole-brain neural activity imaging in fish behaving in a virtual reality environment. Thus, PyZebrascope will help disseminate and develop light-sheet microscopy techniques in the neuroscience community and advance our understanding of whole-brain neural dynamics during animal behaviors.

Perceptions of Cognitive Training Games and Assessment Technologies for Dementia: Acceptability Study With Patient and Public Involvement Workshops.

BACKGROUND: Cognitive training and assessment technologies offer the promise of dementia risk reduction and a more timely diagnosis of dementia, respectively. Cognitive training games may help reduce the lifetime risk of dementia by helping to build cognitive reserve, whereas cognitive assessment technologies offer the opportunity for a more convenient approach to early detection or screening. OBJECTIVE: This study aims to elicit perspectives of potential end users on factors related to the acceptability of cognitive training games and assessment technologies, including their opinions on the meaningfulness of measurement of cognition, barriers to and facilitators of adoption, motivations to use games, and interrelationships with existing health care infrastructure. METHODS: Four linked workshops were conducted with the same group, each focusing on a specific topic: meaningful improvement, learning and motivation, trust in digital diagnosis, and barriers to technology adoption. Participants in the workshops included local involvement team members acting as facilitators and those recruited via Join Dementia Research through a purposive selection and volunteer sampling method. Group activities were recorded, and transcripts were analyzed using thematic analysis with a combination of a priori and data-driven themes. Using a mixed methods approach, we investigated the relationships between the categories of the Capability, Opportunity, and Motivation-Behavior change model along with data-driven themes by measuring the φ coefficient between coded excerpts and ensuring the reliability of our coding scheme by using independent reviewers and assessing interrater reliability. Finally, we explored these themes and their relationships to address our research objectives. RESULTS: In addition to discussions around the capability, motivation, and opportunity categories, several important themes emerged during the workshops: family and friends, cognition and mood, work and hobbies, and technology. Group participants mentioned the importance of functional and objective measures of cognitive change, the social aspect of activities as a motivating factor, and the opportunities and potential shortcomings of digital health care provision. Our quantitative results indicated at least moderate agreement on all but one of the coding schemes and good independence of our coding categories. Positive and statistically significant φ coefficients were observed between several coding themes between categories, including a relatively strong positive φ coefficient between capability and cognition (0.468; P<.001). CONCLUSIONS: The implications for researchers and technology developers include assessing how cognitive training and screening pathways would integrate into existing health care systems; however, further work needs to be undertaken to address barriers to adoption and the potential real-world impact of cognitive training and screening technologies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1007/978-3-030-49065-2_4.

Can fractal dimensions objectivize gastropod shell morphometrics? A case study from Lake Lugu (SW China).

Morphometrics are fundamental for the analysis of size and shape in fossils, particularly because soft parts or DNA are rarely preserved and hard parts such as shells are commonly the only source of information. Geometric morphometrics, that is, landmark analysis, is well established for the description of shape but it exhibits a couple of shortcomings resulting from subjective choices during landmarking (number and position of landmarks) and from difficulties in resolving shape at the level of micro-sculpture.With the aid of high-resolution 3D scanning technology and analyses of fractal dimensions, we test whether such shortcomings of linear and landmark morphometrics can be overcome. As a model group, we selected a clade of modern viviparid gastropods from Lake Lugu, with shells that show a high degree of sculptural variation. Linear and landmark analyses were applied to the same shells in order to establish the fractal dimensions. The genetic diversity of the gastropod clade was assessed.The genetic results suggest that the gastropod clade represents a single species. The results of all morphometric methods applied are in line with the genetic results, which is that no specific morphotype could be delimited. Apart from this overall agreement, landmark and fractal dimension analyses do not correspond to each other but represent data sets with different information. Generally, the fractal dimension values quantify the roughness of the shell surface, the resolution of the 3D scans determining the level. In our approach, we captured the micro-sculpture but not the first-order sculptural elements, which explains that fractal dimension and landmark data are not in phase.We can show that analyzing fractal dimensions of gastropod shells opens a window to more detailed information that can be considered in evolutionary and ecological contexts. We propose that using low-resolution 3D scans may successfully substitute landmark analyses because it overcomes the subjective landmarking. Analyses of 3D scans with higher resolution than used in this study will provide surface roughness information at the mineralogical level. We suggest that fractal dimension analyses of a combination of differently resolved 3D models will significantly improve the quality of shell morphometrics.

Development of a Machine Learning Approach for Local-Scale Ozone Forecasting: Application to Kennewick, WA.

Chemical transport models (CTMs) are widely used for air quality forecasts, but these models require large computational resources and often suffer from a systematic bias that leads to missed poor air pollution events. For example, a CTM-based operational forecasting system for air quality over the Pacific Northwest, called AIRPACT, uses over 100 processors for several hours to provide 48-h forecasts daily, but struggles to capture unhealthy O3 episodes during the summer and early fall, especially over Kennewick, WA. This research developed machine learning (ML) based O3 forecasts for Kennewick, WA to demonstrate an improved forecast capability. We used the 2017-2020 simulated meteorology and O3 observation data from Kennewick as training datasets. The meteorology datasets are from the Weather Research and Forecasting (WRF) meteorological model forecasts produced daily by the University of Washington. Our ozone forecasting system consists of two ML models, ML1 and ML2, to improve predictability: ML1 uses the random forest (RF) classifier and multiple linear regression (MLR) models, and ML2 uses a two-phase RF regression model with best-fit weighting factors. To avoid overfitting, we evaluate the ML forecasting system with the 10-time, 10-fold, and walk-forward cross-validation analysis. Compared to AIRPACT, ML1 improved forecast skill for high-O3 events and captured 5 out of 10 unhealthy O3 events, while AIRPACT and ML2 missed all the unhealthy events. ML2 showed better forecast skill for less elevated-O3 events. Based on this result, we set up our ML modeling framework to use ML1 for high-O3 events and ML2 for less elevated O3 events. Since May 2019, the ML modeling framework has been used to produce daily 72-h O3 forecasts and has provided forecasts via the web for clean air agency and public use: http://ozonematters.com/. Compared to the testing period, the operational forecasting period has not had unhealthy O3 events. Nevertheless, the ML modeling framework demonstrated a reliable forecasting capability at a selected location with much less computational resources. The ML system uses a single processor for minutes compared to the CTM-based forecasting system using more than 100 processors for hours.

Complex population dynamics in a spatial microbial ecosystem with Physarum polycephalum.

This research addresses the interactions between the unicellular slime mold Physarum polycephalum and a red yeast in a spatial ecosystem over week-long imaging experiments. An inverse relationship between the growth rates of both species is shown, where P. polycephalum has positive growth when the red yeast has a negative growth rate and vice versa. The data also captures successional and oscillatory dynamics between both species. An advanced image analysis methodology for semantic segmentation is used to quantify population density over time, for all components of the ecosystem. We suggest that P. polycephalum is capable of exhibiting a sustainable feeding strategy by depositing a nutritive slime trail, allowing yeast to serve as a periodic food source. This opens a new direction of P. polycephalum research, where the population dynamics of spatial ecosystems can be readily quantified and complex ecological dynamics can be studied.

SNT: a unifying toolbox for quantification of neuronal anatomy.

SNT is an end-to-end framework for neuronal morphometry and whole-brain connectomics that supports tracing, proof-editing, visualization, quantification and modeling of neuroanatomy. With an open architecture, a large user base, community-based documentation, support for complex imagery and several model organisms, SNT is a flexible resource for the broad neuroscience community. SNT is both a desktop application and multi-language scripting library, and it is available through the Fiji distribution of ImageJ.

Active perception during angiogenesis: filopodia speed up Notch selection of tip cells in silico and in vivo.

How do cells make efficient collective decisions during tissue morphogenesis? Humans and other organisms use feedback between movement and sensing known as 'sensorimotor coordination' or 'active perception' to inform behaviour, but active perception has not before been investigated at a cellular level within organs. Here we provide the first proof of concept in silico/in vivo study demonstrating that filopodia (actin-rich, dynamic, finger-like cell membrane protrusions) play an unexpected role in speeding up collective endothelial decisions during the time-constrained process of 'tip cell' selection during blood vessel formation (angiogenesis). We first validate simulation predictions in vivo with live imaging of zebrafish intersegmental vessel growth. Further simulation studies then indicate the effect is due to the coupled positive feedback between movement and sensing on filopodia conferring a bistable switch-like property to Notch lateral inhibition, ensuring tip selection is a rapid and robust process. We then employ measures from computational neuroscience to assess whether filopodia function as a primitive (basal) form of active perception and find evidence in support. By viewing cell behaviour through the 'basal cognitive lens' we acquire a fresh perspective on the tip cell selection process, revealing a hidden, yet vital time-keeping role for filopodia. Finally, we discuss a myriad of new and exciting research directions stemming from our conceptual approach to interpreting cell behaviour. This article is part of the theme issue 'Basal cognition: multicellularity, neurons and the cognitive lens'.

Mouse retinal cell behaviour in space and time using light sheet fluorescence microscopy.

As the general population ages, more people are affected by eye diseases, such as retinopathies. It is therefore critical to improve imaging of eye disease mouse models. Here, we demonstrate that 1) rapid, quantitative 3D and 4D (time lapse) imaging of cellular and subcellular processes in the mouse eye is feasible, with and without tissue clearing, using light-sheet fluorescent microscopy (LSFM); 2) flat-mounting retinas for confocal microscopy significantly distorts tissue morphology, confirmed by quantitative correlative LSFM-Confocal imaging of vessels; 3) LSFM readily reveals new features of even well-studied eye disease mouse models, such as the oxygen-induced retinopathy (OIR) model, including a previously unappreciated 'knotted' morphology to pathological vascular tufts, abnormal cell motility and altered filopodia dynamics when live-imaged. We conclude that quantitative 3D/4D LSFM imaging and analysis has the potential to advance our understanding of the eye, in particular pathological, neurovascular, degenerative processes.

Eye diseases affect millions of people worldwide and can have devasting effects on people's lives. To find new treatments, scientists need to understand more about how these diseases arise and how they progress. This is challenging and progress has been held back by limitations in current techniques for looking at the eye. Currently, the most commonly used method is called confocal imaging, which is slow and distorts the tissue. Distortion happens because confocal imaging requires that thin slices of eye tissue from mice used in experiments are flattened on slides; this makes it hard to accurately visualize three-dimensional structures in the eye. New methods are emerging that may help. One promising method is called light-sheet fluorescent microscopy (or LSFM for short). This method captures three-dimensional images of the blood vessels and cells in the eye. It is much faster than confocal imaging and allows scientists to image tissues without slicing or flattening them. This could lead to more accurate three-dimensional images of eye disease. Now, Prahst et al. show that LSFM can quickly produce highly detailed, three-dimensional images of mouse retinas, from the smallest parts of cells to the entire eye. The technique also identified new features in a well-studied model of retina damage caused by excessive oxygen exposure in young mice. Previous studies of this model suggested the disease caused blood vessels in the eye to balloon, hinting that drugs that shrink blood vessels would help. But using LSFM, Prahst et al. revealed that these blood vessels actually take on a twisted and knotted shape. This suggests that treatments that untangle the vessels rather than shrink them are needed. The experiments show that LSFM is a valuable tool for studying eye diseases, that may help scientists learn more about how these diseases arise and develop. These new insights may one day lead to better tests and treatments for eye diseases.

A large pool of actively cycling progenitors orchestrates self-renewal and injury repair of an ectodermal appendage.

The classical model of tissue renewal posits that small numbers of quiescent stem cells (SCs) give rise to proliferating transit-amplifying cells before terminal differentiation. However, many organs house pools of SCs with proliferative and differentiation potentials that diverge from this template. Resolving SC identity and organization is therefore central to understanding tissue renewal. Here, using a combination of single-cell RNA sequencing (scRNA-seq), mouse genetics and tissue injury approaches, we uncover cellular hierarchies and mechanisms that underlie the maintenance and repair of the continuously growing mouse incisor. Our results reveal that, during homeostasis, a group of actively cycling epithelial progenitors generates enamel-producing ameloblasts and adjacent layers of non-ameloblast cells. After injury, tissue repair was achieved through transient increases in progenitor-cell proliferation and through direct conversion of Notch1-expressing cells to ameloblasts. We elucidate epithelial SC identity, position and function, providing a mechanistic basis for the homeostasis and repair of a fast-turnover ectodermal appendage.

Feel the noise: Mid-air ultrasound haptics as a novel human-vehicle interaction paradigm.

Focussed ultrasound can be used to create the sensation of touch in mid-air. Combined with gestures, this can provide haptic feedback to guide users, thereby overcoming the lack of agency associated with pure gestural interfaces, and reducing the need for vision - it is therefore particularly apropos of the driving domain. In a counter-balanced 2 × 2 driving simulator study, a traditional in-vehicle touchscreen was compared with a virtual mid-air gestural interface, both with and without ultrasound haptics. Forty-eight experienced drivers (28 male, 20 female) undertook representative in-vehicle tasks - discrete target selections and continuous slider-bar manipulations - whilst driving. Results show that haptifying gestures with ultrasound was particularly effective in reducing visual demand (number of long glances and mean off-road glance time), and increasing performance (shortest interaction times, highest number of correct responses and least 'overshoots') associated with continuous tasks. In contrast, for discrete, target-selections, the touchscreen enabled the highest accuracy and quickest responses, particularly when combined with haptic feedback to guide interactions, although this also increased visual demand. Subjectively, the gesture interfaces invited higher ratings of arousal compared to the more familiar touch-surface technology, and participants indicated the lowest levels of workload (highest performance, lowest frustration) associated with the gesture-haptics interface. In addition, gestures were preferred by participants for continuous tasks. The study shows practical utility and clear potential for the use of haptified gestures in the automotive domain.

Escalation of Memory Length in Finite Populations.

The escalation of complexity is a commonly cited benefit of coevolutionary systems, but computational simulations generally fail to demonstrate this capacity to a satisfactory degree. We draw on a macroevolutionary theory of escalation to develop a set of criteria for coevolutionary systems to exhibit escalation of strategic complexity. By expanding on a previously developed model of the evolution of memory length for cooperative strategies by Kristian Lindgren, we resolve previously observed limitations on the escalation of memory length by extending operators of evolutionary variation. We present long-term coevolutionary simulations showing that larger population sizes tend to support greater escalation of complexity than smaller ones do. Additionally, we investigate the sensitivity of escalation during transitions of complexity. The Lindgren model has often been used to argue that the escalation of competitive coevolution has intrinsic limitations. Our simulations show that coevolutionary arms races can continue to escalate in computational simulations given sufficient population sizes.

Multi-sample SPIM image acquisition, processing and analysis of vascular growth in zebrafish.

To quantitatively understand biological processes that occur over many hours or days, it is desirable to image multiple samples simultaneously, and automatically process and analyse the resulting datasets. Here, we present a complete multi-sample preparation, imaging, processing and analysis workflow to determine the development of the vascular volume in zebrafish. Up to five live embryos were mounted and imaged simultaneously over several days using selective plane illumination microscopy (SPIM). The resulting large imagery dataset of several terabytes was processed in an automated manner on a high-performance computer cluster and segmented using a novel segmentation approach that uses images of red blood cells as training data. This analysis yielded a precise quantification of growth characteristics of the whole vascular network, head vasculature and tail vasculature over development. Our multi-sample platform demonstrates effective upgrades to conventional single-sample imaging platforms and paves the way for diverse quantitative long-term imaging studies.

Acquisition and reconstruction of 4D surfaces of axolotl embryos with the flipping stage robotic microscope.

We have designed and constructed a Flipping Stage for a light microscope that can view the whole exterior surface of a 2 mm diameter developing axolotl salamander embryo. It works by rapidly inverting the bottom-heavy embryo, imaging it as it rights itself. The images are then montaged to reconstruct the whole 3D surface versus time, for a full 4D record of the surface. Imaging early stage axolotl development will help discover how cell differentiation and movement takes place in the early embryo. For example, the switch from ectodermal to neural plate cells takes place on the top, animal surface portion the egg/embryo and can be observed using the flipping stage microscope. Detailed pictures of the whole surface need to be obtained so that cell tracking and event histories, such as cell divisions and participation in differentiation waves, of individual cells can be recorded. Imaging the whole exterior of the eggs/embryos will allow for the analysis of cell behavior and the forces the cells experience in their natural setting in the intact or manipulated embryo. This will give insights into embryogenesis, development, developmental disruptions, birth defects, cell differentiation and tissue engineering.

FunImageJ: a Lisp framework for scientific image processing.

Summary: FunImageJ is a Lisp framework for scientific image processing built upon the ImageJ software ecosystem. The framework provides a natural functional-style for programming, while accounting for the performance requirements necessary in big data processing commonly encountered in biological image analysis. Availability and implementation: Freely available plugin to Fiji (http://fiji.sc/#download). Installation and use instructions available at http://imagej.net/FunImageJ. Contact: kharrington@uidaho.edu. Supplementary information: Supplementary data are available at Bioinformatics online.